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+Metadata-Version: 2.4
+Name: numpy
+Version: 2.4.3
+Summary: Fundamental package for array computing in Python
+Author: Travis E. Oliphant et al.
+Maintainer-Email: NumPy Developers <numpy-discussion@python.org>
+License-Expression: BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0
+License-File: LICENSE.txt
+License-File: numpy/_core/include/numpy/libdivide/LICENSE.txt
+License-File: numpy/_core/src/common/pythoncapi-compat/COPYING
+License-File: numpy/_core/src/highway/LICENSE
+License-File: numpy/_core/src/multiarray/dragon4_LICENSE.txt
+License-File: numpy/_core/src/npysort/x86-simd-sort/LICENSE.md
+License-File: numpy/_core/src/umath/svml/LICENSE
+License-File: numpy/fft/pocketfft/LICENSE.md
+License-File: numpy/linalg/lapack_lite/LICENSE.txt
+License-File: numpy/ma/LICENSE
+License-File: numpy/random/LICENSE.md
+License-File: numpy/random/src/distributions/LICENSE.md
+License-File: numpy/random/src/mt19937/LICENSE.md
+License-File: numpy/random/src/pcg64/LICENSE.md
+License-File: numpy/random/src/philox/LICENSE.md
+License-File: numpy/random/src/sfc64/LICENSE.md
+License-File: numpy/random/src/splitmix64/LICENSE.md
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Topic :: Software Development
+Classifier: Topic :: Scientific/Engineering
+Classifier: Typing :: Typed
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: homepage, https://numpy.org
+Project-URL: documentation, https://numpy.org/doc/
+Project-URL: source, https://github.com/numpy/numpy
+Project-URL: download, https://pypi.org/project/numpy/#files
+Project-URL: tracker, https://github.com/numpy/numpy/issues
+Project-URL: release notes, https://numpy.org/doc/stable/release
+Requires-Python: >=3.11
+Description-Content-Type: text/markdown
+
+<h1 align="center">
+<img src="https://raw.githubusercontent.com/numpy/numpy/main/branding/logo/primary/numpylogo.svg" width="300">
+</h1><br>
+
+
+[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](
+https://numfocus.org)
+[![PyPI Downloads](https://img.shields.io/pypi/dm/numpy.svg?label=PyPI%20downloads)](
+https://pypi.org/project/numpy/)
+[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/numpy.svg?label=Conda%20downloads)](
+https://anaconda.org/conda-forge/numpy)
+[![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
+https://stackoverflow.com/questions/tagged/numpy)
+[![Nature Paper](https://img.shields.io/badge/DOI-10.1038%2Fs41586--020--2649--2-blue)](
+https://doi.org/10.1038/s41586-020-2649-2)
+[![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=numpy)](https://insights.linuxfoundation.org/project/numpy)
+[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/numpy/numpy/badge)](https://securityscorecards.dev/viewer/?uri=github.com/numpy/numpy)
+[![Typing](https://img.shields.io/pypi/types/numpy)](https://pypi.org/project/numpy/)
+
+
+NumPy is the fundamental package for scientific computing with Python.
+
+- **Website:** https://numpy.org
+- **Documentation:** https://numpy.org/doc
+- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
+- **Source code:** https://github.com/numpy/numpy
+- **Contributing:** https://numpy.org/devdocs/dev/index.html
+- **Bug reports:** https://github.com/numpy/numpy/issues
+- **Report a security vulnerability:** https://tidelift.com/docs/security
+
+It provides:
+
+- a powerful N-dimensional array object
+- sophisticated (broadcasting) functions
+- tools for integrating C/C++ and Fortran code
+- useful linear algebra, Fourier transform, and random number capabilities
+
+Testing:
+
+NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:
+
+    python -c "import numpy, sys; sys.exit(numpy.test() is False)"
+
+Code of Conduct
+----------------------
+
+NumPy is a community-driven open source project developed by a diverse group of
+[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong
+commitment to creating an open, inclusive, and positive community. Please read the
+[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact
+with others in a way that makes our community thrive.
+
+Call for Contributions
+----------------------
+
+The NumPy project welcomes your expertise and enthusiasm!
+
+Small improvements or fixes are always appreciated. If you are considering larger contributions
+to the source code, please contact us through the [mailing
+list](https://mail.python.org/mailman/listinfo/numpy-discussion) first.
+
+Writing code isn’t the only way to contribute to NumPy. You can also:
+- review pull requests
+- help us stay on top of new and old issues
+- develop tutorials, presentations, and other educational materials
+- maintain and improve [our website](https://github.com/numpy/numpy.org)
+- develop graphic design for our brand assets and promotional materials
+- translate website content
+- help with outreach and onboard new contributors
+- write grant proposals and help with other fundraising efforts
+
+For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). 
+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the mailing list or here, on GitHub, by opening a new issue or leaving a
+comment on a relevant issue that is already open.
+
+Our preferred channels of communication are all public, but if you’d like to
+speak to us in private first, contact our community coordinators at
+numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for
+an invitation).
+
+We also have a biweekly community call, details of which are announced on the
+mailing list. You are very welcome to join.
+
+If you are new to contributing to open source, [this
+guide](https://opensource.guide/how-to-contribute/) helps explain why, what,
+and how to successfully get involved.
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diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/REQUESTED
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@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp312-cp312-manylinux_2_27_x86_64
+Tag: cp312-cp312-manylinux_2_28_x86_64
+
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/entry_points.txt b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/entry_points.txt
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+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/entry_points.txt
@@ -0,0 +1,13 @@
+[pkg_config]
+numpy = numpy._core.lib.pkgconfig
+
+[array_api]
+numpy = numpy
+
+[pyinstaller40]
+hook-dirs = numpy:_pyinstaller_hooks_dir
+
+[console_scripts]
+f2py = numpy.f2py.f2py2e:main
+numpy-config = numpy._configtool:main
+
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/LICENSE.txt b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/LICENSE.txt
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index 0000000000000000000000000000000000000000..029edda290588ba5cc924f6544076473c2119407
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/LICENSE.txt
@@ -0,0 +1,935 @@
+Copyright (c) 2005-2025, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+----
+
+
+----
+
+This binary distribution of NumPy also bundles the following software:
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+
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diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/include/numpy/libdivide/LICENSE.txt b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/include/numpy/libdivide/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d72a7c388d406191f2b3113efa5f89916a39d9b2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/include/numpy/libdivide/LICENSE.txt
@@ -0,0 +1,21 @@
+  zlib License
+  ------------
+
+  Copyright (C) 2010 - 2019 ridiculous_fish, <libdivide@ridiculousfish.com>
+  Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>
+
+  This software is provided 'as-is', without any express or implied
+  warranty.  In no event will the authors be held liable for any damages
+  arising from the use of this software.
+
+  Permission is granted to anyone to use this software for any purpose,
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diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/common/pythoncapi-compat/COPYING b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/common/pythoncapi-compat/COPYING
new file mode 100644
index 0000000000000000000000000000000000000000..59a39fb411050556ec1b321a42e870417134671a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/common/pythoncapi-compat/COPYING
@@ -0,0 +1,14 @@
+BSD Zero Clause License
+
+Copyright Contributors to the pythoncapi_compat project.
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+Permission to use, copy, modify, and/or distribute this software for any
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diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/highway/LICENSE b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/highway/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1af4f15ca709c0e74e610c09fe91022f354aea58
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/highway/LICENSE
@@ -0,0 +1,371 @@
+This project is primarily dual-licensed under your choice of either the Apache
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+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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+ b. Affirmer offers the Work as-is and makes no representations or
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+ c. Affirmer disclaims responsibility for clearing rights of other persons
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\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/multiarray/dragon4_LICENSE.txt b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/multiarray/dragon4_LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7bd49e7074a8efbd14496e0b82ec51f14cc6dffe
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/multiarray/dragon4_LICENSE.txt
@@ -0,0 +1,27 @@
+Copyright (c) 2014 Ryan Juckett
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
+
+dragon4.c|h h contains a modified version of Ryan Juckett's Dragon4
+implementation, obtained from https://www.ryanjuckett.com,
+which has been ported from C++ to C and which has
+modifications specific to printing floats in numpy.
+
+Ryan Juckett's original code was under the Zlib license; he gave numpy
+permission to include it under the MIT license instead.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/npysort/x86-simd-sort/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/npysort/x86-simd-sort/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e32165ed474ee2b9877a6716beceb0271643b5a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/npysort/x86-simd-sort/LICENSE.md
@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2022, Intel. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/umath/svml/LICENSE b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/umath/svml/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..4723d4ea009e5af16664576c0a4d99d41cb11cc9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/_core/src/umath/svml/LICENSE
@@ -0,0 +1,30 @@
+Copyright (c) 2005-2021, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/fft/pocketfft/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/fft/pocketfft/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..c3a4c06a92d024e12ab76ce6b20eae815979552e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/fft/pocketfft/LICENSE.md
@@ -0,0 +1,25 @@
+Copyright (C) 2010-2018 Max-Planck-Society
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the documentation and/or
+  other materials provided with the distribution.
+* Neither the name of the copyright holder nor the names of its contributors may
+  be used to endorse or promote products derived from this software without
+  specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/linalg/lapack_lite/LICENSE.txt b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/linalg/lapack_lite/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9b379c9e897351cecb1f31f3061192f4319ac73d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/linalg/lapack_lite/LICENSE.txt
@@ -0,0 +1,48 @@
+Copyright (c) 1992-2013 The University of Tennessee and The University
+                        of Tennessee Research Foundation.  All rights
+                        reserved.
+Copyright (c) 2000-2013 The University of California Berkeley. All
+                        rights reserved.
+Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
+                        reserved.
+
+$COPYRIGHT$
+
+Additional copyrights may follow
+
+$HEADER$
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+- Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+- Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer listed
+  in this license in the documentation and/or other materials
+  provided with the distribution.
+
+- Neither the name of the copyright holders nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+The copyright holders provide no reassurances that the source code
+provided does not infringe any patent, copyright, or any other
+intellectual property rights of third parties.  The copyright holders
+disclaim any liability to any recipient for claims brought against
+recipient by any third party for infringement of that parties
+intellectual property rights.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/ma/LICENSE b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/ma/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b41aae0c89a0f2486843d395f972db759c73c4b8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/ma/LICENSE
@@ -0,0 +1,24 @@
+* Copyright (c) 2006, University of Georgia and Pierre G.F. Gerard-Marchant
+* All rights reserved.
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+*     * Redistributions of source code must retain the above copyright
+*       notice, this list of conditions and the following disclaimer.
+*     * Redistributions in binary form must reproduce the above copyright
+*       notice, this list of conditions and the following disclaimer in the
+*       documentation and/or other materials provided with the distribution.
+*     * Neither the name of the University of Georgia nor the
+*       names of its contributors may be used to endorse or promote products
+*       derived from this software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY
+* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+* DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
+* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6cf1b17e99725556ac56ce3661498df1ee2276a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/LICENSE.md
@@ -0,0 +1,71 @@
+**This software is dual-licensed under the The University of Illinois/NCSA
+Open Source License (NCSA) and The 3-Clause BSD License**
+
+# NCSA Open Source License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
+<kevin.k.sheppard@gmail.com>)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/distributions/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/distributions/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..31576ba4b1f26876cae13c0e08b6c7a81b4f8521
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/distributions/LICENSE.md
@@ -0,0 +1,61 @@
+## NumPy
+
+Copyright (c) 2005-2017, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+   copyright notice, this list of conditions and the following
+   disclaimer in the documentation and/or other materials provided
+   with the distribution.
+
+* Neither the name of the NumPy Developers nor the names of any
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+## Julia
+
+The ziggurat methods were derived from Julia.
+
+Copyright (c) 2009-2019: Jeff Bezanson, Stefan Karpinski, Viral B. Shah,
+and other contributors:
+
+https://github.com/JuliaLang/julia/contributors
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/mt19937/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/mt19937/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..f65c3d46e62406a38d984cd0551fe38a298b2d7f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/mt19937/LICENSE.md
@@ -0,0 +1,61 @@
+# MT19937
+
+Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)
+
+The rk_random and rk_seed functions algorithms and the original design of
+the Mersenne Twister RNG:
+
+  Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions
+  are met:
+
+  1. Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+  3. The names of its contributors may not be used to endorse or promote
+  products derived from this software without specific prior written
+  permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER
+OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Original algorithm for the implementation of rk_interval function from
+Richard J. Wagner's implementation of the Mersenne Twister RNG, optimised by
+Magnus Jonsson.
+
+Constants used in the rk_double implementation by Isaku Wada.
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/pcg64/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/pcg64/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..7aac7a51c96abdd62bbec2bf9a2b518c868743ec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/pcg64/LICENSE.md
@@ -0,0 +1,22 @@
+# PCG64
+
+## The MIT License
+
+PCG Random Number Generation for C.
+
+Copyright 2014 Melissa O'Neill <oneill@pcg-random.org>
+
+Permission is hereby granted, free of charge, to any person obtaining 
+a copy of this software and associated documentation files (the "Software"), 
+to deal in the Software without restriction, including without limitation 
+the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in 
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/philox/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/philox/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..9738e44de3b4b7c76d33c8980573577bb83cb828
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/philox/LICENSE.md
@@ -0,0 +1,31 @@
+# PHILOX
+
+Copyright 2010-2012, D. E. Shaw Research.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+  notice, this list of conditions, and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions, and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+* Neither the name of D. E. Shaw Research nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/sfc64/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/sfc64/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..21dd604afe16c2ce8d192a1d5fca9aec6702afee
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/sfc64/LICENSE.md
@@ -0,0 +1,27 @@
+# SFC64
+
+## The MIT License
+
+Adapted from a C++ implementation of Chris Doty-Humphrey's SFC PRNG.
+
+https://gist.github.com/imneme/f1f7821f07cf76504a97f6537c818083
+
+Copyright (c) 2018 Melissa E. O'Neill
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the "Software"),
+to deal in the Software without restriction, including without limitation
+the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/splitmix64/LICENSE.md b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/splitmix64/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c4d73b920f6eb58c5fa18144962bf8956601b35
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy-2.4.3.dist-info/licenses/numpy/random/src/splitmix64/LICENSE.md
@@ -0,0 +1,9 @@
+# SPLITMIX64
+
+Written in 2015 by Sebastiano Vigna (vigna@acm.org)
+
+To the extent possible under law, the author has dedicated all copyright
+and related and neighboring rights to this software to the public domain
+worldwide. This software is distributed without any warranty.
+
+See <http://creativecommons.org/publicdomain/zero/1.0/>.
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0 b/.venv/lib/python3.12/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
new file mode 100644
index 0000000000000000000000000000000000000000..32aeee9a92e063a830909443ffa10a61f8eb4896
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6090048eccc763522c12ef016f81da6b627cb3a044f55cf0479a839c41c0980
+size 2833617
diff --git a/.venv/lib/python3.12/site-packages/numpy.libs/libquadmath-96973f99-934c22de.so.0.0.0 b/.venv/lib/python3.12/site-packages/numpy.libs/libquadmath-96973f99-934c22de.so.0.0.0
new file mode 100644
index 0000000000000000000000000000000000000000..4e1eb5101254f9fcebd5d9b3fec1f6413b0f931c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy.libs/libquadmath-96973f99-934c22de.so.0.0.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ed5137f412781ad7863439fb543613f620b43c32b63292a0029246162f5bbc6
+size 250985
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/random.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/random.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e188eb02893f06faf35b1b5265c83dc24f1d84c9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/random.pyi
@@ -0,0 +1,1546 @@
+import threading
+from collections.abc import Sequence
+from typing import Any, assert_type
+
+import numpy as np
+import numpy.typing as npt
+from numpy.random._generator import Generator
+from numpy.random._mt19937 import MT19937
+from numpy.random._pcg64 import PCG64
+from numpy.random._philox import Philox
+from numpy.random._sfc64 import SFC64
+from numpy.random.bit_generator import SeedlessSeedSequence, SeedSequence
+
+def_rng = np.random.default_rng()
+seed_seq = np.random.SeedSequence()
+mt19937 = np.random.MT19937()
+pcg64 = np.random.PCG64()
+sfc64 = np.random.SFC64()
+philox = np.random.Philox()
+seedless_seq = SeedlessSeedSequence()
+
+assert_type(def_rng, Generator)
+assert_type(mt19937, MT19937)
+assert_type(pcg64, PCG64)
+assert_type(sfc64, SFC64)
+assert_type(philox, Philox)
+assert_type(seed_seq, SeedSequence)
+assert_type(seedless_seq, SeedlessSeedSequence)
+
+mt19937_jumped = mt19937.jumped()
+mt19937_jumped3 = mt19937.jumped(3)
+mt19937_raw = mt19937.random_raw()
+mt19937_raw_arr = mt19937.random_raw(5)
+
+assert_type(mt19937_jumped, MT19937)
+assert_type(mt19937_jumped3, MT19937)
+assert_type(mt19937_raw, int)
+assert_type(mt19937_raw_arr, npt.NDArray[np.uint64])
+assert_type(mt19937.lock, threading.Lock)
+
+pcg64_jumped = pcg64.jumped()
+pcg64_jumped3 = pcg64.jumped(3)
+pcg64_adv = pcg64.advance(3)
+pcg64_raw = pcg64.random_raw()
+pcg64_raw_arr = pcg64.random_raw(5)
+
+assert_type(pcg64_jumped, PCG64)
+assert_type(pcg64_jumped3, PCG64)
+assert_type(pcg64_adv, PCG64)
+assert_type(pcg64_raw, int)
+assert_type(pcg64_raw_arr, npt.NDArray[np.uint64])
+assert_type(pcg64.lock, threading.Lock)
+
+philox_jumped = philox.jumped()
+philox_jumped3 = philox.jumped(3)
+philox_adv = philox.advance(3)
+philox_raw = philox.random_raw()
+philox_raw_arr = philox.random_raw(5)
+
+assert_type(philox_jumped, Philox)
+assert_type(philox_jumped3, Philox)
+assert_type(philox_adv, Philox)
+assert_type(philox_raw, int)
+assert_type(philox_raw_arr, npt.NDArray[np.uint64])
+assert_type(philox.lock, threading.Lock)
+
+sfc64_raw = sfc64.random_raw()
+sfc64_raw_arr = sfc64.random_raw(5)
+
+assert_type(sfc64_raw, int)
+assert_type(sfc64_raw_arr, npt.NDArray[np.uint64])
+assert_type(sfc64.lock, threading.Lock)
+
+assert_type(seed_seq.pool, npt.NDArray[np.uint32])
+assert_type(seed_seq.entropy, int | Sequence[int] | None)
+assert_type(seed_seq.spawn(1), list[np.random.SeedSequence])
+assert_type(seed_seq.generate_state(8, "uint32"), npt.NDArray[np.uint32 | np.uint64])
+assert_type(seed_seq.generate_state(8, "uint64"), npt.NDArray[np.uint32 | np.uint64])
+
+def_gen: np.random.Generator = np.random.default_rng()
+
+D_arr_0p1: npt.NDArray[np.float64] = np.array([0.1])
+D_arr_0p5: npt.NDArray[np.float64] = np.array([0.5])
+D_arr_0p9: npt.NDArray[np.float64] = np.array([0.9])
+D_arr_1p5: npt.NDArray[np.float64] = np.array([1.5])
+I_arr_10: npt.NDArray[np.int_] = np.array([10], dtype=np.int_)
+I_arr_20: npt.NDArray[np.int_] = np.array([20], dtype=np.int_)
+D_arr_like_0p1: list[float] = [0.1]
+D_arr_like_0p5: list[float] = [0.5]
+D_arr_like_0p9: list[float] = [0.9]
+D_arr_like_1p5: list[float] = [1.5]
+I_arr_like_10: list[int] = [10]
+I_arr_like_20: list[int] = [20]
+D_2D_like: list[list[float]] = [[1, 2], [2, 3], [3, 4], [4, 5.1]]
+D_2D: npt.NDArray[np.float64] = np.array(D_2D_like)
+S_out: npt.NDArray[np.float32] = np.empty(1, dtype=np.float32)
+D_out: npt.NDArray[np.float64] = np.empty(1)
+
+assert_type(def_gen.standard_normal(), float)
+assert_type(def_gen.standard_normal(dtype=np.float32), float)
+assert_type(def_gen.standard_normal(dtype="float32"), float)
+assert_type(def_gen.standard_normal(dtype="double"), float)
+assert_type(def_gen.standard_normal(dtype=np.float64), float)
+assert_type(def_gen.standard_normal(size=None), float)
+assert_type(def_gen.standard_normal(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_normal(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_normal(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.random(), float)
+assert_type(def_gen.random(dtype=np.float32), float)
+assert_type(def_gen.random(dtype="float32"), float)
+assert_type(def_gen.random(dtype="double"), float)
+assert_type(def_gen.random(dtype=np.float64), float)
+assert_type(def_gen.random(size=None), float)
+assert_type(def_gen.random(size=1), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.random(size=1, dtype=np.float64), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.random(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.random(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_cauchy(), float)
+assert_type(def_gen.standard_cauchy(size=None), float)
+assert_type(def_gen.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_exponential(), float)
+assert_type(def_gen.standard_exponential(method="inv"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float32), float)
+assert_type(def_gen.standard_exponential(dtype="float32"), float)
+assert_type(def_gen.standard_exponential(dtype="double"), float)
+assert_type(def_gen.standard_exponential(dtype=np.float64), float)
+assert_type(def_gen.standard_exponential(size=None), float)
+assert_type(def_gen.standard_exponential(size=None, method="inv"), float)
+assert_type(def_gen.standard_exponential(size=1, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float32), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="f4", method="inv"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_exponential(size=1, dtype=np.float64, method="inv"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="f8"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64"), npt.NDArray[np.float64])
+assert_type(def_gen.standard_exponential(size=1, dtype="float64", out=D_out), npt.NDArray[np.float64])
+
+assert_type(def_gen.zipf(1.5), int)
+assert_type(def_gen.zipf(1.5, size=None), int)
+assert_type(def_gen.zipf(1.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_1p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5), npt.NDArray[np.int64])
+assert_type(def_gen.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.weibull(0.5), float)
+assert_type(def_gen.weibull(0.5, size=None), float)
+assert_type(def_gen.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_t(0.5), float)
+assert_type(def_gen.standard_t(0.5, size=None), float)
+assert_type(def_gen.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.poisson(0.5), int)
+assert_type(def_gen.poisson(0.5, size=None), int)
+assert_type(def_gen.poisson(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.power(0.5), float)
+assert_type(def_gen.power(0.5, size=None), float)
+assert_type(def_gen.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.pareto(0.5), float)
+assert_type(def_gen.pareto(0.5, size=None), float)
+assert_type(def_gen.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.chisquare(0.5), float)
+assert_type(def_gen.chisquare(0.5, size=None), float)
+assert_type(def_gen.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.exponential(0.5), float)
+assert_type(def_gen.exponential(0.5, size=None), float)
+assert_type(def_gen.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.geometric(0.5), int)
+assert_type(def_gen.geometric(0.5, size=None), int)
+assert_type(def_gen.geometric(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.logseries(0.5), int)
+assert_type(def_gen.logseries(0.5, size=None), int)
+assert_type(def_gen.logseries(0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.rayleigh(0.5), float)
+assert_type(def_gen.rayleigh(0.5, size=None), float)
+assert_type(def_gen.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.standard_gamma(0.5), float)
+assert_type(def_gen.standard_gamma(0.5, size=None), float)
+assert_type(def_gen.standard_gamma(0.5, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=None, dtype="float32"), float)
+assert_type(def_gen.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype="f4"), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(0.5, size=1, dtype="float32", out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, dtype=np.float32, out=S_out), npt.NDArray[np.float32])
+assert_type(def_gen.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(0.5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, out=D_out), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.standard_gamma(D_arr_like_0p5, size=1, out=D_out, dtype=np.float64), npt.NDArray[np.float64])
+
+assert_type(def_gen.vonmises(0.5, 0.5), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=None), float)
+assert_type(def_gen.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.wald(0.5, 0.5), float)
+assert_type(def_gen.wald(0.5, 0.5, size=None), float)
+assert_type(def_gen.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.uniform(0.5, 0.5), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=None), float)
+assert_type(def_gen.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.beta(0.5, 0.5), float)
+assert_type(def_gen.beta(0.5, 0.5, size=None), float)
+assert_type(def_gen.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.f(0.5, 0.5), float)
+assert_type(def_gen.f(0.5, 0.5, size=None), float)
+assert_type(def_gen.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gamma(0.5, 0.5), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=None), float)
+assert_type(def_gen.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.gumbel(0.5, 0.5), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=None), float)
+assert_type(def_gen.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.laplace(0.5, 0.5), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=None), float)
+assert_type(def_gen.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.logistic(0.5, 0.5), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=None), float)
+assert_type(def_gen.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.lognormal(0.5, 0.5), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=None), float)
+assert_type(def_gen.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(def_gen.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.normal(0.5, 0.5), float)
+assert_type(def_gen.normal(0.5, 0.5, size=None), float)
+assert_type(def_gen.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.triangular(0.1, 0.5, 0.9), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(def_gen.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(def_gen.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(def_gen.binomial(10, 0.5), int)
+assert_type(def_gen.binomial(10, 0.5, size=None), int)
+assert_type(def_gen.binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.negative_binomial(10, 0.5), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=None), int)
+assert_type(def_gen.negative_binomial(10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.int64])
+
+assert_type(def_gen.hypergeometric(20, 20, 10), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=None), int)
+assert_type(def_gen.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.int64])
+assert_type(def_gen.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.int64])
+
+I_int64_100: npt.NDArray[np.int64] = np.array([100], dtype=np.int64)
+
+assert_type(def_gen.integers(0, 100), np.int64)
+assert_type(def_gen.integers(100), np.int64)
+assert_type(def_gen.integers([100]), npt.NDArray[np.int64])
+assert_type(def_gen.integers(0, [100]), npt.NDArray[np.int64])
+
+I_bool_low: npt.NDArray[np.bool] = np.array([0], dtype=np.bool)
+I_bool_low_like: list[int] = [0]
+I_bool_high_open: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+I_bool_high_closed: npt.NDArray[np.bool] = np.array([1], dtype=np.bool)
+
+assert_type(def_gen.integers(2, dtype=bool), bool)
+assert_type(def_gen.integers(0, 2, dtype=bool), bool)
+assert_type(def_gen.integers(1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(0, 1, dtype=bool, endpoint=True), bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=bool, endpoint=True), npt.NDArray[np.bool])
+
+assert_type(def_gen.integers(2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(0, 2, dtype=np.bool), np.bool)
+assert_type(def_gen.integers(1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(0, 1, dtype=np.bool, endpoint=True), np.bool)
+assert_type(def_gen.integers(I_bool_low_like, 1, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(I_bool_low, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+assert_type(def_gen.integers(0, I_bool_high_closed, dtype=np.bool, endpoint=True), npt.NDArray[np.bool])
+
+I_u1_low: npt.NDArray[np.uint8] = np.array([0], dtype=np.uint8)
+I_u1_low_like: list[int] = [0]
+I_u1_high_open: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+I_u1_high_closed: npt.NDArray[np.uint8] = np.array([255], dtype=np.uint8)
+
+assert_type(def_gen.integers(256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="u1"), np.uint8)
+assert_type(def_gen.integers(255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="u1", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="u1", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype="uint8"), np.uint8)
+assert_type(def_gen.integers(255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype="uint8", endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype="uint8", endpoint=True), npt.NDArray[np.uint8])
+
+assert_type(def_gen.integers(256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(0, 256, dtype=np.uint8), np.uint8)
+assert_type(def_gen.integers(255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(0, 255, dtype=np.uint8, endpoint=True), np.uint8)
+assert_type(def_gen.integers(I_u1_low_like, 255, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(I_u1_low, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+assert_type(def_gen.integers(0, I_u1_high_closed, dtype=np.uint8, endpoint=True), npt.NDArray[np.uint8])
+
+I_u2_low: npt.NDArray[np.uint16] = np.array([0], dtype=np.uint16)
+I_u2_low_like: list[int] = [0]
+I_u2_high_open: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+I_u2_high_closed: npt.NDArray[np.uint16] = np.array([65535], dtype=np.uint16)
+
+assert_type(def_gen.integers(65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="u2"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="u2", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="u2", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype="uint16"), np.uint16)
+assert_type(def_gen.integers(65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype="uint16", endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype="uint16", endpoint=True), npt.NDArray[np.uint16])
+
+assert_type(def_gen.integers(65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(def_gen.integers(65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(0, 65535, dtype=np.uint16, endpoint=True), np.uint16)
+assert_type(def_gen.integers(I_u2_low_like, 65535, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(I_u2_low, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+assert_type(def_gen.integers(0, I_u2_high_closed, dtype=np.uint16, endpoint=True), npt.NDArray[np.uint16])
+
+I_u4_low: npt.NDArray[np.uint32] = np.array([0], dtype=np.uint32)
+I_u4_low_like: list[int] = [0]
+I_u4_high_open: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+I_u4_high_closed: npt.NDArray[np.uint32] = np.array([4294967295], dtype=np.uint32)
+
+assert_type(def_gen.integers(4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.int_), np.int_)
+assert_type(def_gen.integers(4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.int_, endpoint=True), np.int_)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.int_, endpoint=True), npt.NDArray[np.int_])
+
+assert_type(def_gen.integers(4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="u4", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="u4", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype="uint32", endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype="uint32", endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(def_gen.integers(4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint32, endpoint=True), np.uint32)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint32, endpoint=True), npt.NDArray[np.uint32])
+
+assert_type(def_gen.integers(4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(def_gen.integers(4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(0, 4294967295, dtype=np.uint, endpoint=True), np.uint)
+assert_type(def_gen.integers(I_u4_low_like, 4294967295, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(I_u4_low, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+assert_type(def_gen.integers(0, I_u4_high_closed, dtype=np.uint, endpoint=True), npt.NDArray[np.uint])
+
+I_u8_low: npt.NDArray[np.uint64] = np.array([0], dtype=np.uint64)
+I_u8_low_like: list[int] = [0]
+I_u8_high_open: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+I_u8_high_closed: npt.NDArray[np.uint64] = np.array([18446744073709551615], dtype=np.uint64)
+
+assert_type(def_gen.integers(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="u8", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="u8", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype="uint64", endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype="uint64", endpoint=True), npt.NDArray[np.uint64])
+
+assert_type(def_gen.integers(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(def_gen.integers(18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(0, 18446744073709551615, dtype=np.uint64, endpoint=True), np.uint64)
+assert_type(def_gen.integers(I_u8_low_like, 18446744073709551615, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(I_u8_low, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+assert_type(def_gen.integers(0, I_u8_high_closed, dtype=np.uint64, endpoint=True), npt.NDArray[np.uint64])
+
+I_i1_low: npt.NDArray[np.int8] = np.array([-128], dtype=np.int8)
+I_i1_low_like: list[int] = [-128]
+I_i1_high_open: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+I_i1_high_closed: npt.NDArray[np.int8] = np.array([127], dtype=np.int8)
+
+assert_type(def_gen.integers(128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="i1"), np.int8)
+assert_type(def_gen.integers(127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="i1", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="i1", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype="int8"), np.int8)
+assert_type(def_gen.integers(127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype="int8", endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype="int8", endpoint=True), npt.NDArray[np.int8])
+
+assert_type(def_gen.integers(128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(-128, 128, dtype=np.int8), np.int8)
+assert_type(def_gen.integers(127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(-128, 127, dtype=np.int8, endpoint=True), np.int8)
+assert_type(def_gen.integers(I_i1_low_like, 127, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(I_i1_low, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+assert_type(def_gen.integers(-128, I_i1_high_closed, dtype=np.int8, endpoint=True), npt.NDArray[np.int8])
+
+I_i2_low: npt.NDArray[np.int16] = np.array([-32768], dtype=np.int16)
+I_i2_low_like: list[int] = [-32768]
+I_i2_high_open: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+I_i2_high_closed: npt.NDArray[np.int16] = np.array([32767], dtype=np.int16)
+
+assert_type(def_gen.integers(32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="i2"), np.int16)
+assert_type(def_gen.integers(32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="i2", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="i2", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype="int16"), np.int16)
+assert_type(def_gen.integers(32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype="int16", endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype="int16", endpoint=True), npt.NDArray[np.int16])
+
+assert_type(def_gen.integers(32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(def_gen.integers(32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(-32768, 32767, dtype=np.int16, endpoint=True), np.int16)
+assert_type(def_gen.integers(I_i2_low_like, 32767, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(I_i2_low, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+assert_type(def_gen.integers(-32768, I_i2_high_closed, dtype=np.int16, endpoint=True), npt.NDArray[np.int16])
+
+I_i4_low: npt.NDArray[np.int32] = np.array([-2147483648], dtype=np.int32)
+I_i4_low_like: list[int] = [-2147483648]
+I_i4_high_open: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+I_i4_high_closed: npt.NDArray[np.int32] = np.array([2147483647], dtype=np.int32)
+
+assert_type(def_gen.integers(2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="i4", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="i4", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(def_gen.integers(2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype="int32", endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype="int32", endpoint=True), npt.NDArray[np.int32])
+
+assert_type(def_gen.integers(2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(def_gen.integers(2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(-2147483648, 2147483647, dtype=np.int32, endpoint=True), np.int32)
+assert_type(def_gen.integers(I_i4_low_like, 2147483647, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(I_i4_low, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+assert_type(def_gen.integers(-2147483648, I_i4_high_closed, dtype=np.int32, endpoint=True), npt.NDArray[np.int32])
+
+I_i8_low: npt.NDArray[np.int64] = np.array([-9223372036854775808], dtype=np.int64)
+I_i8_low_like: list[int] = [-9223372036854775808]
+I_i8_high_open: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+I_i8_high_closed: npt.NDArray[np.int64] = np.array([9223372036854775807], dtype=np.int64)
+
+assert_type(def_gen.integers(9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="i8", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="i8", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype="int64", endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype="int64", endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.integers(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(def_gen.integers(9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(-9223372036854775808, 9223372036854775807, dtype=np.int64, endpoint=True), np.int64)
+assert_type(def_gen.integers(I_i8_low_like, 9223372036854775807, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(I_i8_low, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+assert_type(def_gen.integers(-9223372036854775808, I_i8_high_closed, dtype=np.int64, endpoint=True), npt.NDArray[np.int64])
+
+assert_type(def_gen.bit_generator, np.random.BitGenerator)
+
+assert_type(def_gen.bytes(2), bytes)
+
+assert_type(def_gen.choice(5), int)
+assert_type(def_gen.choice(5, 3), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, replace=True), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.int64])
+assert_type(def_gen.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.int64])
+
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(def_gen.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(def_gen.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(def_gen.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial([[10], [20]], [1 / 6.0] * 6, size=(2, 2)), npt.NDArray[np.int64])
+assert_type(def_gen.multinomial(np.array([[10], [20]]), np.array([0.5, 0.5]), size=(2, 2)), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=4), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, size=(4, 7)), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric([3, 5, 7], 2, method="count"), npt.NDArray[np.int64])
+assert_type(def_gen.multivariate_hypergeometric(np.array([3, 5, 7]), 2, method="marginals"), npt.NDArray[np.int64])
+
+assert_type(def_gen.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(def_gen.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(def_gen.permutation(10), npt.NDArray[np.int64])
+assert_type(def_gen.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(def_gen.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(def_gen.permutation(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D_like, out=D_2D), npt.NDArray[Any])
+assert_type(def_gen.permuted(D_2D, axis=1, out=D_2D), npt.NDArray[Any])
+
+assert_type(def_gen.shuffle(np.arange(10)), None)
+assert_type(def_gen.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(def_gen.shuffle(D_2D, axis=1), None)
+
+assert_type(np.random.Generator(pcg64), np.random.Generator)
+assert_type(def_gen.__str__(), str)
+assert_type(def_gen.__repr__(), str)
+assert_type(def_gen.__setstate__(dict(def_gen.bit_generator.state)), None)
+
+# RandomState
+random_st: np.random.RandomState = np.random.RandomState()
+
+assert_type(random_st.standard_normal(), float)
+assert_type(random_st.standard_normal(size=None), float)
+assert_type(random_st.standard_normal(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.random(), float)
+assert_type(random_st.random(size=None), float)
+assert_type(random_st.random(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_cauchy(), float)
+assert_type(random_st.standard_cauchy(size=None), float)
+assert_type(random_st.standard_cauchy(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_exponential(), float)
+assert_type(random_st.standard_exponential(size=None), float)
+assert_type(random_st.standard_exponential(size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.zipf(1.5), int)
+assert_type(random_st.zipf(1.5, size=None), int)
+assert_type(random_st.zipf(1.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_1p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5), npt.NDArray[np.long])
+assert_type(random_st.zipf(D_arr_like_1p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.weibull(0.5), float)
+assert_type(random_st.weibull(0.5, size=None), float)
+assert_type(random_st.weibull(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.weibull(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_t(0.5), float)
+assert_type(random_st.standard_t(0.5, size=None), float)
+assert_type(random_st.standard_t(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_t(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.poisson(0.5), int)
+assert_type(random_st.poisson(0.5, size=None), int)
+assert_type(random_st.poisson(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.poisson(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.power(0.5), float)
+assert_type(random_st.power(0.5, size=None), float)
+assert_type(random_st.power(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.power(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.pareto(0.5), float)
+assert_type(random_st.pareto(0.5, size=None), float)
+assert_type(random_st.pareto(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.pareto(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.chisquare(0.5), float)
+assert_type(random_st.chisquare(0.5, size=None), float)
+assert_type(random_st.chisquare(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.chisquare(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.exponential(0.5), float)
+assert_type(random_st.exponential(0.5, size=None), float)
+assert_type(random_st.exponential(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.exponential(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.geometric(0.5), int)
+assert_type(random_st.geometric(0.5, size=None), int)
+assert_type(random_st.geometric(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.geometric(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.logseries(0.5), int)
+assert_type(random_st.logseries(0.5, size=None), int)
+assert_type(random_st.logseries(0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.logseries(D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.rayleigh(0.5), float)
+assert_type(random_st.rayleigh(0.5, size=None), float)
+assert_type(random_st.rayleigh(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.rayleigh(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.standard_gamma(0.5), float)
+assert_type(random_st.standard_gamma(0.5, size=None), float)
+assert_type(random_st.standard_gamma(0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.standard_gamma(D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.vonmises(0.5, 0.5), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=None), float)
+assert_type(random_st.vonmises(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.vonmises(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.wald(0.5, 0.5), float)
+assert_type(random_st.wald(0.5, 0.5, size=None), float)
+assert_type(random_st.wald(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.wald(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.wald(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.uniform(0.5, 0.5), float)
+assert_type(random_st.uniform(0.5, 0.5, size=None), float)
+assert_type(random_st.uniform(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.uniform(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.beta(0.5, 0.5), float)
+assert_type(random_st.beta(0.5, 0.5, size=None), float)
+assert_type(random_st.beta(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.beta(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.beta(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.f(0.5, 0.5), float)
+assert_type(random_st.f(0.5, 0.5, size=None), float)
+assert_type(random_st.f(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.f(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.f(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gamma(0.5, 0.5), float)
+assert_type(random_st.gamma(0.5, 0.5, size=None), float)
+assert_type(random_st.gamma(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gamma(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.gumbel(0.5, 0.5), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=None), float)
+assert_type(random_st.gumbel(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.gumbel(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.laplace(0.5, 0.5), float)
+assert_type(random_st.laplace(0.5, 0.5, size=None), float)
+assert_type(random_st.laplace(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.laplace(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.logistic(0.5, 0.5), float)
+assert_type(random_st.logistic(0.5, 0.5, size=None), float)
+assert_type(random_st.logistic(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.logistic(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.lognormal(0.5, 0.5), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=None), float)
+assert_type(random_st.lognormal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.lognormal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_chisquare(0.5, 0.5), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=None), float)
+assert_type(random_st.noncentral_chisquare(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_chisquare(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.normal(0.5, 0.5), float)
+assert_type(random_st.normal(0.5, 0.5, size=None), float)
+assert_type(random_st.normal(0.5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, 0.5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, 0.5), npt.NDArray[np.float64])
+assert_type(random_st.normal(0.5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_0p5, D_arr_0p5, size=1), npt.NDArray[np.float64])
+assert_type(random_st.normal(D_arr_like_0p5, D_arr_like_0p5, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.triangular(0.1, 0.5, 0.9), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.triangular(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.triangular(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=None), float)
+assert_type(random_st.noncentral_f(0.1, 0.5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, 0.5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.1, D_arr_0p5, 0.9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, 0.5, D_arr_0p9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(0.5, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, 0.9), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_0p1, D_arr_0p5, D_arr_0p9, size=1), npt.NDArray[np.float64])
+assert_type(random_st.noncentral_f(D_arr_like_0p1, D_arr_like_0p5, D_arr_like_0p9, size=1), npt.NDArray[np.float64])
+
+assert_type(random_st.binomial(10, 0.5), int)
+assert_type(random_st.binomial(10, 0.5, size=None), int)
+assert_type(random_st.binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.negative_binomial(10, 0.5), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=None), int)
+assert_type(random_st.negative_binomial(10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, 0.5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, 0.5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_10, D_arr_0p5, size=1), npt.NDArray[np.long])
+assert_type(random_st.negative_binomial(I_arr_like_10, D_arr_like_0p5, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.hypergeometric(20, 20, 10), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=None), int)
+assert_type(random_st.hypergeometric(20, 20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, 20, I_arr_like_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_20, 10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, 20, I_arr_10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, 10), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_20, I_arr_20, I_arr_10, size=1), npt.NDArray[np.long])
+assert_type(random_st.hypergeometric(I_arr_like_20, I_arr_like_20, I_arr_like_10, size=1), npt.NDArray[np.long])
+
+assert_type(random_st.randint(0, 100), int)
+assert_type(random_st.randint(100), int)
+assert_type(random_st.randint([100]), npt.NDArray[np.long])
+assert_type(random_st.randint(0, [100]), npt.NDArray[np.long])
+
+assert_type(random_st.randint(2, dtype=bool), bool)
+assert_type(random_st.randint(0, 2, dtype=bool), bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(0, 2, dtype=np.bool), np.bool)
+assert_type(random_st.randint(I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(I_bool_low, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+assert_type(random_st.randint(0, I_bool_high_open, dtype=np.bool), npt.NDArray[np.bool])
+
+assert_type(random_st.randint(256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="u1"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="u1"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(0, 256, dtype="uint8"), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype="uint8"), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(0, 256, dtype=np.uint8), np.uint8)
+assert_type(random_st.randint(I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(I_u1_low, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+assert_type(random_st.randint(0, I_u1_high_open, dtype=np.uint8), npt.NDArray[np.uint8])
+
+assert_type(random_st.randint(65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="u2"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="u2"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype="uint16"), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype="uint16"), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(0, 65536, dtype=np.uint16), np.uint16)
+assert_type(random_st.randint(I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(I_u2_low, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+assert_type(random_st.randint(0, I_u2_high_open, dtype=np.uint16), npt.NDArray[np.uint16])
+
+assert_type(random_st.randint(4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="u4"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="u4"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype="uint32"), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype="uint32"), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint32), np.uint32)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint32), npt.NDArray[np.uint32])
+
+assert_type(random_st.randint(4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(0, 4294967296, dtype=np.uint), np.uint)
+assert_type(random_st.randint(I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(I_u4_low, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+assert_type(random_st.randint(0, I_u4_high_open, dtype=np.uint), npt.NDArray[np.uint])
+
+assert_type(random_st.randint(18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="u8"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="u8"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype="uint64"), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype="uint64"), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(0, 18446744073709551616, dtype=np.uint64), np.uint64)
+assert_type(random_st.randint(I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(I_u8_low, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+assert_type(random_st.randint(0, I_u8_high_open, dtype=np.uint64), npt.NDArray[np.uint64])
+
+assert_type(random_st.randint(128, dtype="i1"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="i1"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="i1"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype="int8"), np.int8)
+assert_type(random_st.randint(-128, 128, dtype="int8"), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype="int8"), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(-128, 128, dtype=np.int8), np.int8)
+assert_type(random_st.randint(I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(I_i1_low, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+assert_type(random_st.randint(-128, I_i1_high_open, dtype=np.int8), npt.NDArray[np.int8])
+
+assert_type(random_st.randint(32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="i2"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="i2"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype="int16"), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype="int16"), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(-32768, 32768, dtype=np.int16), np.int16)
+assert_type(random_st.randint(I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(I_i2_low, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+assert_type(random_st.randint(-32768, I_i2_high_open, dtype=np.int16), npt.NDArray[np.int16])
+
+assert_type(random_st.randint(2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="i4"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="i4"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype="int32"), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype="int32"), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int32), np.int32)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int32), npt.NDArray[np.int32])
+
+assert_type(random_st.randint(2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(-2147483648, 2147483648, dtype=np.int_), np.int_)
+assert_type(random_st.randint(I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(I_i4_low, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+assert_type(random_st.randint(-2147483648, I_i4_high_open, dtype=np.int_), npt.NDArray[np.int_])
+
+assert_type(random_st.randint(9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="i8"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="i8"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype="int64"), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype="int64"), npt.NDArray[np.int64])
+
+assert_type(random_st.randint(9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(-9223372036854775808, 9223372036854775808, dtype=np.int64), np.int64)
+assert_type(random_st.randint(I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(I_i8_low, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+assert_type(random_st.randint(-9223372036854775808, I_i8_high_open, dtype=np.int64), npt.NDArray[np.int64])
+
+assert_type(random_st._bit_generator, np.random.BitGenerator)
+
+assert_type(random_st.bytes(2), bytes)
+
+assert_type(random_st.choice(5), int)
+assert_type(random_st.choice(5, 3), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, replace=True), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5), npt.NDArray[np.long])
+assert_type(random_st.choice(5, 3, p=[1 / 5] * 5, replace=False), npt.NDArray[np.long])
+
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"]), Any)
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, p=[1 / 4] * 4), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=True), npt.NDArray[Any])
+assert_type(random_st.choice(["pooh", "rabbit", "piglet", "Christopher"], 3, replace=False, p=np.array([1 / 8, 1 / 8, 1 / 2, 1 / 4])), npt.NDArray[Any])
+
+assert_type(random_st.dirichlet([0.5, 0.5]), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5])), npt.NDArray[np.float64])
+assert_type(random_st.dirichlet(np.array([0.5, 0.5]), size=3), npt.NDArray[np.float64])
+
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, np.array([0.5, 0.5])), npt.NDArray[np.long])
+assert_type(random_st.multinomial(20, [1 / 6.0] * 6, size=2), npt.NDArray[np.long])
+
+assert_type(random_st.multivariate_normal([0.0], [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal(np.array([0.0]), [[1.0]]), npt.NDArray[np.float64])
+assert_type(random_st.multivariate_normal([0.0], np.array([[1.0]])), npt.NDArray[np.float64])
+
+assert_type(random_st.permutation(10), npt.NDArray[np.long])
+assert_type(random_st.permutation([1, 2, 3, 4]), npt.NDArray[Any])
+assert_type(random_st.permutation(np.array([1, 2, 3, 4])), npt.NDArray[Any])
+assert_type(random_st.permutation(D_2D), npt.NDArray[Any])
+
+assert_type(random_st.shuffle(np.arange(10)), None)
+assert_type(random_st.shuffle([1, 2, 3, 4, 5]), None)
+assert_type(random_st.shuffle(D_2D), None)
+
+assert_type(np.random.RandomState(pcg64), np.random.RandomState)
+assert_type(np.random.RandomState(0), np.random.RandomState)
+assert_type(np.random.RandomState([0, 1, 2]), np.random.RandomState)
+assert_type(random_st.__str__(), str)
+assert_type(random_st.__repr__(), str)
+random_st_state = random_st.__getstate__()
+assert_type(random_st_state, dict[str, Any])
+assert_type(random_st.__setstate__(random_st_state), None)
+assert_type(random_st.seed(), None)
+assert_type(random_st.seed(1), None)
+assert_type(random_st.seed([0, 1]), None)
+random_st_get_state = random_st.get_state()
+assert_type(random_st_state, dict[str, Any])
+random_st_get_state_legacy = random_st.get_state(legacy=True)
+assert_type(random_st_get_state_legacy, dict[str, Any] | tuple[str, npt.NDArray[np.uint32], int, int, float])
+assert_type(random_st.set_state(random_st_get_state), None)
+
+assert_type(random_st.rand(), float)
+assert_type(random_st.rand(1), npt.NDArray[np.float64])
+assert_type(random_st.rand(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.randn(), float)
+assert_type(random_st.randn(1), npt.NDArray[np.float64])
+assert_type(random_st.randn(1, 2), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(), float)
+assert_type(random_st.random_sample(1), npt.NDArray[np.float64])
+assert_type(random_st.random_sample(size=(1, 2)), npt.NDArray[np.float64])
+
+assert_type(random_st.tomaxint(), int)
+assert_type(random_st.tomaxint(1), npt.NDArray[np.int64])
+assert_type(random_st.tomaxint((1,)), npt.NDArray[np.int64])
+
+assert_type(np.random.mtrand.set_bit_generator(pcg64), None)
+assert_type(np.random.mtrand.get_bit_generator(), np.random.BitGenerator)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/rec.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/rec.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aacf217e42072cacc9a245d7d7faef8cb340f2c2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/rec.pyi
@@ -0,0 +1,171 @@
+import io
+from typing import Any, TypeAlias, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+_RecArray: TypeAlias = np.recarray[tuple[Any, ...], np.dtype[np.record]]
+
+AR_i8: npt.NDArray[np.int64]
+REC_AR_V: _RecArray
+AR_LIST: list[npt.NDArray[np.int64]]
+
+record: np.record
+file_obj: io.BufferedIOBase
+
+assert_type(np.rec.format_parser(
+    formats=[np.float64, np.int64, np.bool],
+    names=["f8", "i8", "?"],
+    titles=None,
+    aligned=True,
+), np.rec.format_parser)
+assert_type(np.rec.format_parser.dtype, np.dtype[np.void])
+
+assert_type(record.field_a, Any)
+assert_type(record.field_b, Any)
+assert_type(record["field_a"], Any)
+assert_type(record["field_b"], Any)
+assert_type(record.pprint(), str)
+record.field_c = 5
+
+assert_type(REC_AR_V.field(0), Any)
+assert_type(REC_AR_V.field("field_a"), Any)
+assert_type(REC_AR_V.field(0, AR_i8), None)
+assert_type(REC_AR_V.field("field_a", AR_i8), None)
+assert_type(REC_AR_V["field_a"], npt.NDArray[Any])
+assert_type(REC_AR_V.field_a, Any)
+assert_type(REC_AR_V.__array_finalize__(object()), None)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        formats=[np.float64, np.int64, np.bool],
+        order="K",
+        byteorder="|",
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.recarray(
+        shape=(10, 5),
+        dtype=[("f8", np.float64), ("i8", np.int64)],
+        strides=(5, 5),
+    ),
+    np.recarray,
+)
+
+assert_type(np.rec.fromarrays(AR_LIST), np.recarray)
+assert_type(
+    np.rec.fromarrays(AR_LIST, dtype=np.int64),
+    np.recarray,
+)
+assert_type(
+    np.rec.fromarrays(
+        AR_LIST,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.fromrecords((1, 1.5)),
+    _RecArray
+)
+
+assert_type(
+    np.rec.fromrecords(
+        [(1, 1.5)],
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.fromrecords(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.fromstring(
+        b"(1, 1.5)",
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.fromstring(
+        REC_AR_V,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.fromfile(
+        "test_file.txt",
+        dtype=[("i8", np.int64), ("f8", np.float64)],
+    ),
+    np.recarray,
+)
+
+assert_type(
+    np.rec.fromfile(
+        file_obj,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    _RecArray,
+)
+
+assert_type(np.rec.array(AR_i8), np.recarray[tuple[Any, ...], np.dtype[np.int64]])
+
+assert_type(
+    np.rec.array([(1, 1.5)], dtype=[("i8", np.int64), ("f8", np.float64)]),
+    np.recarray,
+)
+
+assert_type(
+    np.rec.array(
+        [(1, 1.5)],
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"]
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        dtype=np.float64,
+        shape=(10, 3),
+    ),
+    np.recarray,
+)
+
+assert_type(
+    np.rec.array(
+        None,
+        formats=[np.int64, np.float64],
+        names=["i8", "f8"],
+        shape=(10, 3),
+    ),
+    _RecArray,
+)
+
+assert_type(
+    np.rec.array(file_obj, dtype=np.float64),
+    np.recarray,
+)
+
+assert_type(
+    np.rec.array(file_obj, formats=[np.int64, np.float64], names=["i8", "f8"]),
+    _RecArray,
+)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/scalars.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..67444e33dfc389e164092afee1423ac29a16ed44
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/scalars.pyi
@@ -0,0 +1,191 @@
+from typing import Any, Literal, TypeAlias, assert_type
+
+import numpy as np
+
+_1: TypeAlias = Literal[1]
+
+b: np.bool
+u8: np.uint64
+i8: np.int64
+f8: np.float64
+c8: np.complex64
+c16: np.complex128
+m: np.timedelta64
+U: np.str_
+S: np.bytes_
+V: np.void
+O: np.object_  # cannot exists at runtime
+
+array_nd: np.ndarray[Any, Any]
+array_0d: np.ndarray[tuple[()], Any]
+array_2d_2x2: np.ndarray[tuple[Literal[2], Literal[2]], Any]
+
+assert_type(c8.real, np.float32)
+assert_type(c8.imag, np.float32)
+
+assert_type(c8.real.real, np.float32)
+assert_type(c8.real.imag, np.float32)
+
+assert_type(c8.itemsize, int)
+assert_type(c8.shape, tuple[()])
+assert_type(c8.strides, tuple[()])
+
+assert_type(c8.ndim, Literal[0])
+assert_type(c8.size, Literal[1])
+
+assert_type(c8.squeeze(), np.complex64)
+assert_type(c8.byteswap(), np.complex64)
+assert_type(c8.transpose(), np.complex64)
+
+assert_type(c8.dtype, np.dtype[np.complex64])
+
+assert_type(c8.real, np.float32)
+assert_type(c16.imag, np.float64)
+
+assert_type(np.str_("foo"), np.str_)
+
+assert_type(V[0], Any)
+assert_type(V["field1"], Any)
+assert_type(V[["field1", "field2"]], np.void)
+V[0] = 5
+
+# Aliases
+assert_type(np.bool_(), np.bool[Literal[False]])
+assert_type(np.byte(), np.byte)
+assert_type(np.short(), np.short)
+assert_type(np.intc(), np.intc)
+assert_type(np.intp(), np.intp)
+assert_type(np.int_(), np.int_)
+assert_type(np.long(), np.long)
+assert_type(np.longlong(), np.longlong)
+
+assert_type(np.ubyte(), np.ubyte)
+assert_type(np.ushort(), np.ushort)
+assert_type(np.uintc(), np.uintc)
+assert_type(np.uintp(), np.uintp)
+assert_type(np.uint(), np.uint)
+assert_type(np.ulong(), np.ulong)
+assert_type(np.ulonglong(), np.ulonglong)
+
+assert_type(np.half(), np.half)
+assert_type(np.single(), np.single)
+assert_type(np.double(), np.double)
+assert_type(np.longdouble(), np.longdouble)
+
+assert_type(np.csingle(), np.csingle)
+assert_type(np.cdouble(), np.cdouble)
+assert_type(np.clongdouble(), np.clongdouble)
+
+assert_type(b.item(), bool)
+assert_type(i8.item(), int)
+assert_type(u8.item(), int)
+assert_type(f8.item(), float)
+assert_type(c16.item(), complex)
+assert_type(U.item(), str)
+assert_type(S.item(), bytes)
+
+assert_type(b.tolist(), bool)
+assert_type(i8.tolist(), int)
+assert_type(u8.tolist(), int)
+assert_type(f8.tolist(), float)
+assert_type(c16.tolist(), complex)
+assert_type(U.tolist(), str)
+assert_type(S.tolist(), bytes)
+
+assert_type(b.ravel(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.ravel(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.ravel(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.ravel(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.ravel(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.ravel(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.ravel(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.flatten(), np.ndarray[tuple[int], np.dtype[np.bool]])
+assert_type(i8.flatten(), np.ndarray[tuple[int], np.dtype[np.int64]])
+assert_type(u8.flatten(), np.ndarray[tuple[int], np.dtype[np.uint64]])
+assert_type(f8.flatten(), np.ndarray[tuple[int], np.dtype[np.float64]])
+assert_type(c16.flatten(), np.ndarray[tuple[int], np.dtype[np.complex128]])
+assert_type(U.flatten(), np.ndarray[tuple[int], np.dtype[np.str_]])
+assert_type(S.flatten(), np.ndarray[tuple[int], np.dtype[np.bytes_]])
+
+assert_type(b.reshape(()), np.bool)
+assert_type(i8.reshape([]), np.int64)
+assert_type(b.reshape(1), np.ndarray[tuple[_1], np.dtype[np.bool]])
+assert_type(i8.reshape(-1), np.ndarray[tuple[_1], np.dtype[np.int64]])
+assert_type(u8.reshape(1, 1), np.ndarray[tuple[_1, _1], np.dtype[np.uint64]])
+assert_type(f8.reshape(1, -1), np.ndarray[tuple[_1, _1], np.dtype[np.float64]])
+assert_type(c16.reshape(1, 1, 1), np.ndarray[tuple[_1, _1, _1], np.dtype[np.complex128]])
+assert_type(U.reshape(1, 1, 1, 1), np.ndarray[tuple[_1, _1, _1, _1], np.dtype[np.str_]])
+assert_type(
+    S.reshape(1, 1, 1, 1, 1),
+    np.ndarray[
+        # len(shape) >= 5
+        tuple[_1, _1, _1, _1, _1, *tuple[_1, ...]],
+        np.dtype[np.bytes_],
+    ],
+)
+
+assert_type(i8.astype(float), Any)
+assert_type(i8.astype(np.float64), np.float64)
+
+assert_type(i8.view(), np.int64)
+assert_type(i8.view(np.float64), np.float64)
+assert_type(i8.view(float), Any)
+assert_type(i8.view(np.float64, np.ndarray), np.float64)
+
+assert_type(i8.getfield(float), Any)
+assert_type(i8.getfield(np.float64), np.float64)
+assert_type(i8.getfield(np.float64, 8), np.float64)
+
+assert_type(f8.as_integer_ratio(), tuple[int, int])
+assert_type(f8.is_integer(), bool)
+assert_type(f8.__trunc__(), int)
+assert_type(f8.__getformat__("float"), str)
+assert_type(f8.hex(), str)
+assert_type(np.float64.fromhex("0x0.0p+0"), np.float64)
+
+assert_type(f8.__getnewargs__(), tuple[float])
+assert_type(c16.__getnewargs__(), tuple[float, float])
+
+assert_type(i8.numerator, np.int64)
+assert_type(i8.denominator, Literal[1])
+assert_type(u8.numerator, np.uint64)
+assert_type(u8.denominator, Literal[1])
+assert_type(m.numerator, np.timedelta64)
+assert_type(m.denominator, Literal[1])
+
+assert_type(round(i8), int)
+assert_type(round(i8, 3), np.int64)
+assert_type(round(u8), int)
+assert_type(round(u8, 3), np.uint64)
+assert_type(round(f8), int)
+assert_type(round(f8, 3), np.float64)
+
+assert_type(f8.__ceil__(), int)
+assert_type(f8.__floor__(), int)
+
+assert_type(i8.is_integer(), Literal[True])
+
+assert_type(O.real, np.object_)
+assert_type(O.imag, np.object_)
+assert_type(int(O), int)
+assert_type(float(O), float)
+assert_type(complex(O), complex)
+
+# These fail fail because of a mypy __new__ bug:
+# https://github.com/python/mypy/issues/15182
+# According to the typing spec, the following statements are valid, see
+# https://typing.readthedocs.io/en/latest/spec/constructors.html#new-method
+
+# assert_type(np.object_(), None)
+# assert_type(np.object_(None), None)
+# assert_type(np.object_(array_nd), np.ndarray[Any, np.dtype[np.object_]])
+# assert_type(np.object_([]), npt.NDArray[np.object_])
+# assert_type(np.object_(()), npt.NDArray[np.object_])
+# assert_type(np.object_(range(4)), npt.NDArray[np.object_])
+# assert_type(np.object_(+42), int)
+# assert_type(np.object_(1 / 137), float)
+# assert_type(np.object_('Developers! ' * (1 << 6)), str)
+# assert_type(np.object_(object()), object)
+# assert_type(np.object_({False, True, NotADirectoryError}), set[Any])
+# assert_type(np.object_({'spam': 'food', 'ham': 'food'}), dict[str, str])
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2406a39f9682b66623bd9879afdfcfe311c5f869
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape.pyi
@@ -0,0 +1,13 @@
+from typing import Any, NamedTuple, assert_type
+
+import numpy as np
+
+# Subtype of tuple[int, int]
+class XYGrid(NamedTuple):
+    x_axis: int
+    y_axis: int
+
+arr: np.ndarray[XYGrid, Any]
+
+# Test shape property matches shape typevar
+assert_type(arr.shape, XYGrid)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e409a53bcef95ad549769011cf0a7d28ea78320e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/shape_base.pyi
@@ -0,0 +1,52 @@
+from typing import Any, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+i8: np.int64
+f8: np.float64
+
+AR_b: npt.NDArray[np.bool]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+AR_LIKE_f8: list[float]
+
+assert_type(np.take_along_axis(AR_f8, AR_i8, axis=1), npt.NDArray[np.float64])
+assert_type(np.take_along_axis(f8, AR_i8, axis=None), npt.NDArray[np.float64])
+
+assert_type(np.put_along_axis(AR_f8, AR_i8, "1.0", axis=1), None)
+
+assert_type(np.expand_dims(AR_i8, 2), npt.NDArray[np.int64])
+assert_type(np.expand_dims(AR_LIKE_f8, 2), npt.NDArray[Any])
+
+assert_type(np.column_stack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.column_stack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.dstack([AR_i8]), npt.NDArray[np.int64])
+assert_type(np.dstack([AR_LIKE_f8]), npt.NDArray[Any])
+
+assert_type(np.array_split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.array_split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.split(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.split(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.hsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.hsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.vsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.vsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.dsplit(AR_i8, [3, 5, 6, 10]), list[npt.NDArray[np.int64]])
+assert_type(np.dsplit(AR_LIKE_f8, [3, 5, 6, 10]), list[npt.NDArray[Any]])
+
+assert_type(np.kron(AR_b, AR_b), npt.NDArray[np.bool])
+assert_type(np.kron(AR_b, AR_i8), npt.NDArray[np.signedinteger])
+assert_type(np.kron(AR_f8, AR_f8), npt.NDArray[np.floating])
+
+assert_type(np.tile(AR_i8, 5), npt.NDArray[np.int64])
+assert_type(np.tile(AR_LIKE_f8, [2, 2]), npt.NDArray[Any])
+
+assert_type(np.unstack(AR_i8, axis=0), tuple[npt.NDArray[np.int64], ...])
+assert_type(np.unstack(AR_LIKE_f8, axis=0), tuple[npt.NDArray[Any], ...])
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8fde9b8ae30dd6dd25b1d4f6f68c1321e6f3c0a0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/stride_tricks.pyi
@@ -0,0 +1,27 @@
+from typing import Any, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE_f: list[float]
+interface_dict: dict[str, Any]
+
+assert_type(np.lib.stride_tricks.as_strided(AR_f8), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_LIKE_f), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, strides=(1, 5)), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.as_strided(AR_f8, shape=[9, 20]), npt.NDArray[np.float64])
+
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.lib.stride_tricks.sliding_window_view(AR_f8, [9], axis=1), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_to(AR_f8, 5), npt.NDArray[np.float64])
+assert_type(np.broadcast_to(AR_LIKE_f, (1, 5)), npt.NDArray[Any])
+assert_type(np.broadcast_to(AR_f8, [4, 6], subok=True), npt.NDArray[np.float64])
+
+assert_type(np.broadcast_shapes((1, 2), [3, 1], (3, 2)), tuple[Any, ...])
+assert_type(np.broadcast_shapes((6, 7), (5, 6, 1), 7, (5, 1, 7)), tuple[Any, ...])
+
+assert_type(np.broadcast_arrays(AR_f8, AR_f8), tuple[npt.NDArray[Any], ...])
+assert_type(np.broadcast_arrays(AR_f8, AR_LIKE_f), tuple[npt.NDArray[Any], ...])
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/strings.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/strings.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..18bd252d5ff90f34878ba34fd150bf17093cdc61
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/strings.pyi
@@ -0,0 +1,196 @@
+from typing import TypeAlias, assert_type
+
+import numpy as np
+import numpy._typing as np_t
+import numpy.typing as npt
+
+AR_T_alias: TypeAlias = np.ndarray[np_t._AnyShape, np.dtypes.StringDType]
+AR_TU_alias: TypeAlias = AR_T_alias | npt.NDArray[np.str_]
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+AR_T: AR_T_alias
+
+assert_type(np.strings.equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.not_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.not_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less_equal(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less_equal(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.greater(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.greater(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.less(AR_U, AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_S, AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.less(AR_T, AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.add(AR_U, AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.add(AR_S, AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.add(AR_T, AR_T), AR_T_alias)
+
+assert_type(np.strings.multiply(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.multiply(AR_S, [5, 4, 3]), npt.NDArray[np.bytes_])
+assert_type(np.strings.multiply(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.mod(AR_U, "test"), npt.NDArray[np.str_])
+assert_type(np.strings.mod(AR_S, "test"), npt.NDArray[np.bytes_])
+assert_type(np.strings.mod(AR_T, "test"), AR_T_alias)
+
+assert_type(np.strings.capitalize(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.capitalize(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.capitalize(AR_T), AR_T_alias)
+
+assert_type(np.strings.center(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.center(AR_S, [2, 3, 4], b"a"), npt.NDArray[np.bytes_])
+assert_type(np.strings.center(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.encode(AR_U), npt.NDArray[np.bytes_])
+assert_type(np.strings.encode(AR_T), npt.NDArray[np.bytes_])
+assert_type(np.strings.decode(AR_S), npt.NDArray[np.str_])
+
+assert_type(np.strings.expandtabs(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.expandtabs(AR_S, tabsize=4), npt.NDArray[np.bytes_])
+assert_type(np.strings.expandtabs(AR_T), AR_T_alias)
+
+assert_type(np.strings.ljust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.ljust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.ljust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.ljust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.rjust(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.rjust(AR_S, [4, 3, 1], fillchar=[b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rjust(AR_T, 5), AR_T_alias)
+assert_type(np.strings.rjust(AR_T, [4, 2, 1], fillchar=["a", "b", "c"]), AR_T_alias)
+
+assert_type(np.strings.lstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.lstrip(AR_T), AR_T_alias)
+assert_type(np.strings.lstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.rstrip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.rstrip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.rstrip(AR_T), AR_T_alias)
+assert_type(np.strings.rstrip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.strip(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.strip(AR_S, b"_"), npt.NDArray[np.bytes_])
+assert_type(np.strings.strip(AR_T), AR_T_alias)
+assert_type(np.strings.strip(AR_T, "_"), AR_T_alias)
+
+assert_type(np.strings.count(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.count(AR_T, ["a", "b", "c"], end=9), npt.NDArray[np.int_])
+
+assert_type(np.strings.partition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.partition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.partition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.rpartition(AR_U, "\n"), npt.NDArray[np.str_])
+assert_type(np.strings.rpartition(AR_S, [b"a", b"b", b"c"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.rpartition(AR_T, "\n"), AR_TU_alias)
+
+assert_type(np.strings.replace(AR_U, "_", "-"), npt.NDArray[np.str_])
+assert_type(np.strings.replace(AR_S, [b"_", b""], [b"a", b"b"]), npt.NDArray[np.bytes_])
+assert_type(np.strings.replace(AR_T, "_", "_"), AR_TU_alias)
+
+assert_type(np.strings.lower(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.lower(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.lower(AR_T), AR_T_alias)
+
+assert_type(np.strings.upper(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.upper(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.upper(AR_T), AR_T_alias)
+
+assert_type(np.strings.swapcase(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.swapcase(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.swapcase(AR_T), AR_T_alias)
+
+assert_type(np.strings.title(AR_U), npt.NDArray[np.str_])
+assert_type(np.strings.title(AR_S), npt.NDArray[np.bytes_])
+assert_type(np.strings.title(AR_T), AR_T_alias)
+
+assert_type(np.strings.zfill(AR_U, 5), npt.NDArray[np.str_])
+assert_type(np.strings.zfill(AR_S, [2, 3, 4]), npt.NDArray[np.bytes_])
+assert_type(np.strings.zfill(AR_T, 5), AR_T_alias)
+
+assert_type(np.strings.endswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.endswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.startswith(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.bool])
+assert_type(np.strings.startswith(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.bool])
+
+assert_type(np.strings.find(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.find(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rfind(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rfind(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.index(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.index(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.rindex(AR_U, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_S, [b"a", b"b", b"c"], end=9), npt.NDArray[np.int_])
+assert_type(np.strings.rindex(AR_T, "a", start=[1, 2, 3]), npt.NDArray[np.int_])
+
+assert_type(np.strings.isalpha(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalpha(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isalnum(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isalnum(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdecimal(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdecimal(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isdigit(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isdigit(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.islower(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.islower(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isnumeric(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isnumeric(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isspace(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isspace(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.istitle(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.istitle(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.isupper(AR_U), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_S), npt.NDArray[np.bool])
+assert_type(np.strings.isupper(AR_T), npt.NDArray[np.bool])
+
+assert_type(np.strings.str_len(AR_U), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_S), npt.NDArray[np.int_])
+assert_type(np.strings.str_len(AR_T), npt.NDArray[np.int_])
+
+assert_type(np.strings.translate(AR_U, ""), npt.NDArray[np.str_])
+assert_type(np.strings.translate(AR_S, ""), npt.NDArray[np.bytes_])
+assert_type(np.strings.translate(AR_T, ""), AR_T_alias)
+
+assert_type(np.strings.slice(AR_U, 1, 5, 2), npt.NDArray[np.str_])
+assert_type(np.strings.slice(AR_S, 1, 5, 2), npt.NDArray[np.bytes_])
+assert_type(np.strings.slice(AR_T, 1, 5, 2), AR_T_alias)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/testing.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/testing.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..34fbc5feeb41b930293170c8ba78734a37a9eb07
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/testing.pyi
@@ -0,0 +1,198 @@
+import contextlib
+import re
+import sys
+import types
+import unittest
+import warnings
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any, TypeVar, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+bool_obj: bool
+suppress_obj: np.testing.suppress_warnings  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+FT = TypeVar("FT", bound=Callable[..., Any])
+
+def func() -> int: ...
+
+def func2(
+    x: npt.NDArray[np.number],
+    y: npt.NDArray[np.number],
+) -> npt.NDArray[np.bool]: ...
+
+assert_type(np.testing.KnownFailureException(), np.testing.KnownFailureException)
+assert_type(np.testing.IgnoreException(), np.testing.IgnoreException)
+
+assert_type(
+    np.testing.clear_and_catch_warnings(modules=[np.testing]),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(True),
+    np.testing.clear_and_catch_warnings[list[warnings.WarningMessage]],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(False),
+    np.testing.clear_and_catch_warnings[None],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings(bool_obj),
+    np.testing.clear_and_catch_warnings,
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.class_modules,
+    tuple[types.ModuleType, ...],
+)
+assert_type(
+    np.testing.clear_and_catch_warnings.modules,
+    set[types.ModuleType],
+)
+
+with np.testing.clear_and_catch_warnings(True) as c1:
+    assert_type(c1, list[warnings.WarningMessage])
+with np.testing.clear_and_catch_warnings() as c2:
+    assert_type(c2, None)
+
+assert_type(np.testing.suppress_warnings("once"), np.testing.suppress_warnings)  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+assert_type(np.testing.suppress_warnings()(func), Callable[[], int])  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+assert_type(suppress_obj.filter(RuntimeWarning), None)
+assert_type(suppress_obj.record(RuntimeWarning), list[warnings.WarningMessage])
+with suppress_obj as c3:
+    assert_type(c3, np.testing.suppress_warnings)  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+
+assert_type(np.testing.verbose, int)
+assert_type(np.testing.IS_PYPY, bool)
+assert_type(np.testing.HAS_REFCOUNT, bool)
+assert_type(np.testing.HAS_LAPACK64, bool)
+
+assert_type(np.testing.assert_(1, msg="test"), None)
+assert_type(np.testing.assert_(2, msg=lambda: "test"), None)
+
+if sys.platform == "win32" or sys.platform == "cygwin":
+    assert_type(np.testing.memusage(), int)
+elif sys.platform == "linux":
+    assert_type(np.testing.memusage(), int | None)
+
+assert_type(np.testing.jiffies(), int)
+
+assert_type(np.testing.build_err_msg([0, 1, 2], "test"), str)
+assert_type(np.testing.build_err_msg(range(2), "test", header="header"), str)
+assert_type(np.testing.build_err_msg(np.arange(9).reshape(3, 3), "test", verbose=False), str)
+assert_type(np.testing.build_err_msg("abc", "test", names=["x", "y"]), str)
+assert_type(np.testing.build_err_msg([1.0, 2.0], "test", precision=5), str)
+
+assert_type(np.testing.assert_equal({1}, {1}), None)
+assert_type(np.testing.assert_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_equal(1, 1.0, verbose=True), None)
+
+assert_type(np.testing.print_assert_equal("Test XYZ of func xyz", [0, 1], [0, 1]), None)
+
+assert_type(np.testing.assert_almost_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_almost_equal([1, 2, 3], [1, 2, 3], err_msg="fail"), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_almost_equal(1, 1.0001, decimal=2), None)
+
+assert_type(np.testing.assert_approx_equal(1.0, 1.1), None)
+assert_type(np.testing.assert_approx_equal("1", "2", err_msg="fail"), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0, verbose=True), None)
+assert_type(np.testing.assert_approx_equal(1, 1.0001, significant=2), None)
+
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, header="header"), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, precision=np.int64()), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_nan=False), None)
+assert_type(np.testing.assert_array_compare(func2, AR_i8, AR_f8, equal_inf=True), None)
+
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_equal(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, verbose=True), None)
+assert_type(np.testing.assert_array_almost_equal(AR_i8, AR_f8, decimal=1), None)
+
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, err_msg="test"), None)
+assert_type(np.testing.assert_array_less(AR_i8, AR_f8, verbose=True), None)
+
+assert_type(np.testing.runstring("1 + 1", {}), Any)
+assert_type(np.testing.runstring("int64() + 1", {"int64": np.int64}), Any)
+
+assert_type(np.testing.assert_string_equal("1", "1"), None)
+
+assert_type(np.testing.rundocs(), None)
+assert_type(np.testing.rundocs("test.py"), None)
+assert_type(np.testing.rundocs(Path("test.py"), raise_on_error=True), None)
+
+def func3(a: int) -> bool: ...
+
+assert_type(
+    np.testing.assert_raises(RuntimeWarning),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises(RuntimeWarning, func3, 5), None)
+
+assert_type(
+    np.testing.assert_raises_regex(RuntimeWarning, r"test"),
+    unittest.case._AssertRaisesContext[RuntimeWarning],
+)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, b"test", func3, 5), None)
+assert_type(np.testing.assert_raises_regex(RuntimeWarning, re.compile(b"test"), func3, 5), None)
+
+class Test: ...
+
+def decorate(a: FT) -> FT:
+    return a
+
+assert_type(np.testing.decorate_methods(Test, decorate), None)
+assert_type(np.testing.decorate_methods(Test, decorate, None), None)
+assert_type(np.testing.decorate_methods(Test, decorate, "test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, b"test"), None)
+assert_type(np.testing.decorate_methods(Test, decorate, re.compile("test")), None)
+
+assert_type(np.testing.measure("for i in range(1000): np.sqrt(i**2)"), float)
+assert_type(np.testing.measure(b"for i in range(1000): np.sqrt(i**2)", times=5), float)
+
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, rtol=0.005), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, atol=1), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, equal_nan=True), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, err_msg="err"), None)
+assert_type(np.testing.assert_allclose(AR_i8, AR_f8, verbose=False), None)
+
+assert_type(np.testing.assert_array_almost_equal_nulp(AR_i8, AR_f8, nulp=2), None)
+
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, maxulp=2), npt.NDArray[Any])
+assert_type(np.testing.assert_array_max_ulp(AR_i8, AR_f8, dtype=np.float32), npt.NDArray[Any])
+
+assert_type(np.testing.assert_warns(RuntimeWarning), contextlib._GeneratorContextManager[None])  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+assert_type(np.testing.assert_warns(RuntimeWarning, func3, 5), bool)  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+
+def func4(a: int, b: str) -> bool: ...
+
+assert_type(np.testing.assert_no_warnings(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_warnings(func3, 5), bool)
+assert_type(np.testing.assert_no_warnings(func4, a=1, b="test"), bool)
+assert_type(np.testing.assert_no_warnings(func4, 1, "test"), bool)
+
+assert_type(np.testing.tempdir("test_dir"), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.tempdir(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.tempdir("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.temppath("test_dir", text=True), contextlib._GeneratorContextManager[str])
+assert_type(np.testing.temppath(prefix=b"test"), contextlib._GeneratorContextManager[bytes])
+assert_type(np.testing.temppath("test_dir", dir=Path("here")), contextlib._GeneratorContextManager[str])
+
+assert_type(np.testing.assert_no_gc_cycles(), contextlib._GeneratorContextManager[None])
+assert_type(np.testing.assert_no_gc_cycles(func3, 5), None)
+
+assert_type(np.testing.break_cycles(), None)
+
+assert_type(np.testing.TestCase(), unittest.case.TestCase)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1fd17e36279bb87bf39fb26e3b3148945893f425
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/twodim_base.pyi
@@ -0,0 +1,225 @@
+from typing import Any, TypeAlias, TypeVar, assert_type, type_check_only
+
+import numpy as np
+import numpy.typing as npt
+
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+
+_1D: TypeAlias = tuple[int]
+_2D: TypeAlias = tuple[int, int]
+_ND: TypeAlias = tuple[Any, ...]
+
+_Indices2D: TypeAlias = tuple[
+    np.ndarray[_1D, np.dtype[np.intp]],
+    np.ndarray[_1D, np.dtype[np.intp]],
+]
+
+###
+
+_nd_bool: np.ndarray[_ND, np.dtype[np.bool]]
+_1d_bool: np.ndarray[_1D, np.dtype[np.bool]]
+_2d_bool: np.ndarray[_2D, np.dtype[np.bool]]
+_nd_u64: np.ndarray[_ND, np.dtype[np.uint64]]
+_nd_i64: np.ndarray[_ND, np.dtype[np.int64]]
+_nd_f64: np.ndarray[_ND, np.dtype[np.float64]]
+_nd_c128: np.ndarray[_ND, np.dtype[np.complex128]]
+_nd_obj: np.ndarray[_ND, np.dtype[np.object_]]
+
+_to_nd_bool: list[bool] | list[list[bool]]
+_to_1d_bool: list[bool]
+_to_2d_bool: list[list[bool]]
+
+_to_1d_f64: list[float]
+_to_1d_c128: list[complex]
+
+@type_check_only
+def func1(ar: npt.NDArray[_ScalarT], a: int) -> npt.NDArray[_ScalarT]: ...
+@type_check_only
+def func2(ar: npt.NDArray[np.number], a: str) -> npt.NDArray[np.float64]: ...
+
+@type_check_only
+class _Cube:
+    shape = 3, 4
+    ndim = 2
+
+###
+
+# fliplr
+assert_type(np.fliplr(_nd_bool), np.ndarray[_ND, np.dtype[np.bool]])
+assert_type(np.fliplr(_1d_bool), np.ndarray[_1D, np.dtype[np.bool]])
+assert_type(np.fliplr(_2d_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.fliplr(_to_nd_bool), np.ndarray)
+assert_type(np.fliplr(_to_1d_bool), np.ndarray)
+assert_type(np.fliplr(_to_2d_bool), np.ndarray)
+
+# flipud
+assert_type(np.flipud(_nd_bool), np.ndarray[_ND, np.dtype[np.bool]])
+assert_type(np.flipud(_1d_bool), np.ndarray[_1D, np.dtype[np.bool]])
+assert_type(np.flipud(_2d_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.flipud(_to_nd_bool), np.ndarray)
+assert_type(np.flipud(_to_1d_bool), np.ndarray)
+assert_type(np.flipud(_to_2d_bool), np.ndarray)
+
+# eye
+assert_type(np.eye(10), np.ndarray[_2D, np.dtype[np.float64]])
+assert_type(np.eye(10, M=20, dtype=np.int64), np.ndarray[_2D, np.dtype[np.int64]])
+assert_type(np.eye(10, k=2, dtype=int), np.ndarray[_2D])
+
+# diag
+assert_type(np.diag(_nd_bool), np.ndarray[_ND, np.dtype[np.bool]])
+assert_type(np.diag(_1d_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.diag(_2d_bool), np.ndarray[_1D, np.dtype[np.bool]])
+assert_type(np.diag(_to_nd_bool, k=0), np.ndarray)
+assert_type(np.diag(_to_1d_bool, k=0), np.ndarray[_2D])
+assert_type(np.diag(_to_2d_bool, k=0), np.ndarray[_1D])
+
+# diagflat
+assert_type(np.diagflat(_nd_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.diagflat(_1d_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.diagflat(_2d_bool), np.ndarray[_2D, np.dtype[np.bool]])
+assert_type(np.diagflat(_to_nd_bool, k=0), np.ndarray[_2D])
+assert_type(np.diagflat(_to_1d_bool, k=0), np.ndarray[_2D])
+assert_type(np.diagflat(_to_2d_bool, k=0), np.ndarray[_2D])
+
+# tri
+assert_type(np.tri(10), np.ndarray[_2D, np.dtype[np.float64]])
+assert_type(np.tri(10, M=20, dtype=np.int64), np.ndarray[_2D, np.dtype[np.int64]])
+assert_type(np.tri(10, k=2, dtype=int), np.ndarray[_2D])
+
+# tril
+assert_type(np.tril(_nd_bool), np.ndarray[_ND, np.dtype[np.bool]])
+assert_type(np.tril(_to_nd_bool, k=0), np.ndarray)
+assert_type(np.tril(_to_1d_bool, k=0), np.ndarray)
+assert_type(np.tril(_to_2d_bool, k=0), np.ndarray)
+
+# triu
+assert_type(np.triu(_nd_bool), np.ndarray[_ND, np.dtype[np.bool]])
+assert_type(np.triu(_to_nd_bool, k=0), np.ndarray)
+assert_type(np.triu(_to_1d_bool, k=0), np.ndarray)
+assert_type(np.triu(_to_2d_bool, k=0), np.ndarray)
+
+# vander
+assert_type(np.vander(_nd_bool), np.ndarray[_2D, np.dtype[np.int_]])
+assert_type(np.vander(_nd_u64), np.ndarray[_2D, np.dtype[np.uint64]])
+assert_type(np.vander(_nd_i64, N=2), np.ndarray[_2D, np.dtype[np.int64]])
+assert_type(np.vander(_nd_f64, increasing=True), np.ndarray[_2D, np.dtype[np.float64]])
+assert_type(np.vander(_nd_c128), np.ndarray[_2D, np.dtype[np.complex128]])
+assert_type(np.vander(_nd_obj), np.ndarray[_2D, np.dtype[np.object_]])
+
+# histogram2d
+assert_type(
+    np.histogram2d(_to_1d_f64, _to_1d_f64),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_to_1d_c128, _to_1d_c128),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128 | Any]],
+        np.ndarray[_1D, np.dtype[np.complex128 | Any]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_i64, _nd_bool),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_f64, _nd_i64),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_i64, _nd_f64),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.float64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_f64, _nd_c128, weights=_to_1d_bool),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_f64, _nd_c128, bins=8),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_c128, _nd_f64, bins=(8, 5)),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+        np.ndarray[_1D, np.dtype[np.complex128]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_c128, _nd_i64, bins=_nd_u64),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.uint64]],
+        np.ndarray[_1D, np.dtype[np.uint64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_c128, _nd_c128, bins=(_nd_u64, _nd_u64)),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.uint64]],
+        np.ndarray[_1D, np.dtype[np.uint64]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_c128, _nd_c128, bins=(_nd_bool, 8)),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128 | np.bool]],
+        np.ndarray[_1D, np.dtype[np.complex128 | np.bool]],
+    ],
+)
+assert_type(
+    np.histogram2d(_nd_c128, _nd_c128, bins=(_to_1d_f64, 8)),
+    tuple[
+        np.ndarray[_2D, np.dtype[np.float64]],
+        np.ndarray[_1D, np.dtype[np.complex128 | Any]],
+        np.ndarray[_1D, np.dtype[np.complex128 | Any]],
+    ],
+)
+
+# mask_indices
+assert_type(np.mask_indices(10, func1), _Indices2D)
+assert_type(np.mask_indices(8, func2, "0"), _Indices2D)
+
+# tril_indices
+assert_type(np.tril_indices(3), _Indices2D)
+assert_type(np.tril_indices(3, 1), _Indices2D)
+assert_type(np.tril_indices(3, 1, 2), _Indices2D)
+# tril_indices
+assert_type(np.triu_indices(3), _Indices2D)
+assert_type(np.triu_indices(3, 1), _Indices2D)
+assert_type(np.triu_indices(3, 1, 2), _Indices2D)
+
+# tril_indices_from
+assert_type(np.tril_indices_from(_2d_bool), _Indices2D)
+assert_type(np.tril_indices_from(_Cube()), _Indices2D)
+# triu_indices_from
+assert_type(np.triu_indices_from(_2d_bool), _Indices2D)
+assert_type(np.triu_indices_from(_Cube()), _Indices2D)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/type_check.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..df95da78ffb7a4669b88310739bb710d7d41f6ae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/type_check.pyi
@@ -0,0 +1,67 @@
+from typing import Any, Literal, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+f8: np.float64
+f: float
+
+# NOTE: Avoid importing the platform specific `np.float128` type
+AR_i8: npt.NDArray[np.int64]
+AR_i4: npt.NDArray[np.int32]
+AR_f2: npt.NDArray[np.float16]
+AR_f8: npt.NDArray[np.float64]
+AR_f16: npt.NDArray[np.longdouble]
+AR_c8: npt.NDArray[np.complex64]
+AR_c16: npt.NDArray[np.complex128]
+
+AR_LIKE_f: list[float]
+
+class ComplexObj:
+    real: slice
+    imag: slice
+
+assert_type(np.mintypecode(["f8"], typeset="qfQF"), str)
+
+assert_type(np.real(ComplexObj()), slice)
+assert_type(np.real(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real(AR_c16), npt.NDArray[np.float64])
+assert_type(np.real(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.imag(ComplexObj()), slice)
+assert_type(np.imag(AR_f8), npt.NDArray[np.float64])
+assert_type(np.imag(AR_c16), npt.NDArray[np.float64])
+assert_type(np.imag(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.iscomplex(f8), np.bool)
+assert_type(np.iscomplex(AR_f8), npt.NDArray[np.bool])
+assert_type(np.iscomplex(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.isreal(f8), np.bool)
+assert_type(np.isreal(AR_f8), npt.NDArray[np.bool])
+assert_type(np.isreal(AR_LIKE_f), npt.NDArray[np.bool])
+
+assert_type(np.iscomplexobj(f8), bool)
+assert_type(np.isrealobj(f8), bool)
+
+assert_type(np.nan_to_num(f8), np.float64)
+assert_type(np.nan_to_num(f, copy=True), Any)
+assert_type(np.nan_to_num(AR_f8, nan=1.5), npt.NDArray[np.float64])
+assert_type(np.nan_to_num(AR_LIKE_f, posinf=9999), npt.NDArray[Any])
+
+assert_type(np.real_if_close(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real_if_close(AR_c16), npt.NDArray[np.float64 | np.complex128])
+assert_type(np.real_if_close(AR_c8), npt.NDArray[np.float32 | np.complex64])
+assert_type(np.real_if_close(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.typename("h"), Literal["short"])
+assert_type(np.typename("B"), Literal["unsigned char"])
+assert_type(np.typename("V"), Literal["void"])
+assert_type(np.typename("S1"), Literal["character"])
+
+assert_type(np.common_type(AR_i4), type[np.float64])
+assert_type(np.common_type(AR_f2), type[np.float16])
+assert_type(np.common_type(AR_f2, AR_i4), type[np.float64])
+assert_type(np.common_type(AR_f16, AR_i4), type[np.longdouble])
+assert_type(np.common_type(AR_c8, AR_f2), type[np.complex64])
+assert_type(np.common_type(AR_f2, AR_c8, AR_i4), type[np.complexfloating])
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..77c27eb3b4cabcc70e823ceff2aad7f437c87e9c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunc_config.pyi
@@ -0,0 +1,29 @@
+"""Typing tests for `_core._ufunc_config`."""
+
+from _typeshed import SupportsWrite
+from collections.abc import Callable
+from typing import Any, assert_type
+
+import numpy as np
+
+def func(a: str, b: int) -> None: ...
+
+class Write:
+    def write(self, value: str) -> None: ...
+
+assert_type(np.seterr(all=None), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(divide="ignore"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(over="warn"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(under="call"), np._core._ufunc_config._ErrDict)
+assert_type(np.seterr(invalid="raise"), np._core._ufunc_config._ErrDict)
+assert_type(np.geterr(), np._core._ufunc_config._ErrDict)
+
+assert_type(np.setbufsize(4096), int)
+assert_type(np.getbufsize(), int)
+
+assert_type(np.seterrcall(func), Callable[[str, int], Any] | SupportsWrite[str] | None)
+assert_type(np.seterrcall(Write()), Callable[[str, int], Any] | SupportsWrite[str] | None)
+assert_type(np.geterrcall(), Callable[[str, int], Any] | SupportsWrite[str] | None)
+
+assert_type(np.errstate(call=func, all="call"), np.errstate)
+assert_type(np.errstate(call=Write(), divide="log", over="log"), np.errstate)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..c679b82d28367964f5cc15683fd3e388ced08ce1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufunclike.pyi
@@ -0,0 +1,31 @@
+from typing import assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_O: list[np.object_]
+
+AR_U: npt.NDArray[np.str_]
+
+assert_type(np.fix(AR_LIKE_b), npt.NDArray[np.floating])  # type: ignore[deprecated]
+assert_type(np.fix(AR_LIKE_u), npt.NDArray[np.floating])  # type: ignore[deprecated]
+assert_type(np.fix(AR_LIKE_i), npt.NDArray[np.floating])  # type: ignore[deprecated]
+assert_type(np.fix(AR_LIKE_f), npt.NDArray[np.floating])  # type: ignore[deprecated]
+assert_type(np.fix(AR_LIKE_O), npt.NDArray[np.object_])  # type: ignore[deprecated]
+assert_type(np.fix(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])  # type: ignore[deprecated]
+
+assert_type(np.isposinf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isposinf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
+
+assert_type(np.isneginf(AR_LIKE_b), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_u), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_i), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f), npt.NDArray[np.bool])
+assert_type(np.isneginf(AR_LIKE_f, out=AR_U), npt.NDArray[np.str_])
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eda92f2117c6d64e4f0718f3270f70e103496ea9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/ufuncs.pyi
@@ -0,0 +1,142 @@
+from typing import Any, Literal, NoReturn, assert_type
+
+import numpy as np
+import numpy.typing as npt
+
+i8: np.int64
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_i8: npt.NDArray[np.int64]
+
+assert_type(np.absolute.__doc__, str)
+assert_type(np.absolute.types, list[str])
+
+assert_type(np.absolute.__name__, Literal["absolute"])
+assert_type(np.absolute.__qualname__, Literal["absolute"])
+assert_type(np.absolute.ntypes, Literal[20])
+assert_type(np.absolute.identity, None)
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nin, Literal[1])
+assert_type(np.absolute.nout, Literal[1])
+assert_type(np.absolute.nargs, Literal[2])
+assert_type(np.absolute.signature, None)
+assert_type(np.absolute(f8), Any)
+assert_type(np.absolute(AR_f8), npt.NDArray[Any])
+assert_type(np.absolute.at(AR_f8, AR_i8), None)
+
+assert_type(np.add.__name__, Literal["add"])
+assert_type(np.add.__qualname__, Literal["add"])
+assert_type(np.add.ntypes, Literal[22])
+assert_type(np.add.identity, Literal[0])
+assert_type(np.add.nin, Literal[2])
+assert_type(np.add.nout, Literal[1])
+assert_type(np.add.nargs, Literal[3])
+assert_type(np.add.signature, None)
+assert_type(np.add(f8, f8), Any)
+assert_type(np.add(AR_f8, f8), npt.NDArray[Any])
+assert_type(np.add.at(AR_f8, AR_i8, f8), None)
+assert_type(np.add.reduce(AR_f8, axis=0), Any)
+assert_type(np.add.accumulate(AR_f8), npt.NDArray[Any])
+assert_type(np.add.reduceat(AR_f8, AR_i8), npt.NDArray[Any])
+assert_type(np.add.outer(f8, f8), Any)
+assert_type(np.add.outer(AR_f8, f8), npt.NDArray[Any])
+
+assert_type(np.frexp.__name__, Literal["frexp"])
+assert_type(np.frexp.__qualname__, Literal["frexp"])
+assert_type(np.frexp.ntypes, Literal[4])
+assert_type(np.frexp.identity, None)
+assert_type(np.frexp.nin, Literal[1])
+assert_type(np.frexp.nout, Literal[2])
+assert_type(np.frexp.nargs, Literal[3])
+assert_type(np.frexp.signature, None)
+assert_type(np.frexp(f8), tuple[Any, Any])
+assert_type(np.frexp(AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.divmod.__name__, Literal["divmod"])
+assert_type(np.divmod.__qualname__, Literal["divmod"])
+assert_type(np.divmod.ntypes, Literal[15])
+assert_type(np.divmod.identity, None)
+assert_type(np.divmod.nin, Literal[2])
+assert_type(np.divmod.nout, Literal[2])
+assert_type(np.divmod.nargs, Literal[4])
+assert_type(np.divmod.signature, None)
+assert_type(np.divmod(f8, f8), tuple[Any, Any])
+assert_type(np.divmod(AR_f8, f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
+
+assert_type(np.matmul.__name__, Literal["matmul"])
+assert_type(np.matmul.__qualname__, Literal["matmul"])
+assert_type(np.matmul.ntypes, Literal[19])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul.nin, Literal[2])
+assert_type(np.matmul.nout, Literal[1])
+assert_type(np.matmul.nargs, Literal[3])
+assert_type(np.matmul.signature, Literal["(n?,k),(k,m?)->(n?,m?)"])
+assert_type(np.matmul.identity, None)
+assert_type(np.matmul(AR_f8, AR_f8), Any)
+assert_type(np.matmul(AR_f8, AR_f8, axes=[(0, 1), (0, 1), (0, 1)]), Any)
+
+assert_type(np.vecdot.__name__, Literal["vecdot"])
+assert_type(np.vecdot.__qualname__, Literal["vecdot"])
+assert_type(np.vecdot.ntypes, Literal[19])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot.nin, Literal[2])
+assert_type(np.vecdot.nout, Literal[1])
+assert_type(np.vecdot.nargs, Literal[3])
+assert_type(np.vecdot.signature, Literal["(n),(n)->()"])
+assert_type(np.vecdot.identity, None)
+assert_type(np.vecdot(AR_f8, AR_f8), Any)
+
+assert_type(np.bitwise_count.__name__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.__qualname__, Literal["bitwise_count"])
+assert_type(np.bitwise_count.ntypes, Literal[11])
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count.nin, Literal[1])
+assert_type(np.bitwise_count.nout, Literal[1])
+assert_type(np.bitwise_count.nargs, Literal[2])
+assert_type(np.bitwise_count.signature, None)
+assert_type(np.bitwise_count.identity, None)
+assert_type(np.bitwise_count(i8), Any)
+assert_type(np.bitwise_count(AR_i8), npt.NDArray[Any])
+
+def test_absolute_outer_invalid() -> None:
+    assert_type(np.absolute.outer(AR_f8, AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_frexp_outer_invalid() -> None:
+    assert_type(np.frexp.outer(AR_f8, AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_divmod_outer_invalid() -> None:
+    assert_type(np.divmod.outer(AR_f8, AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_matmul_outer_invalid() -> None:
+    assert_type(np.matmul.outer(AR_f8, AR_f8), NoReturn)  # type: ignore[arg-type]
+
+def test_absolute_reduceat_invalid() -> None:
+    assert_type(np.absolute.reduceat(AR_f8, AR_i8), NoReturn)  # type: ignore[arg-type]
+def test_frexp_reduceat_invalid() -> None:
+    assert_type(np.frexp.reduceat(AR_f8, AR_i8), NoReturn)  # type: ignore[arg-type]
+def test_divmod_reduceat_invalid() -> None:
+    assert_type(np.divmod.reduceat(AR_f8, AR_i8), NoReturn)  # type: ignore[arg-type]
+def test_matmul_reduceat_invalid() -> None:
+    assert_type(np.matmul.reduceat(AR_f8, AR_i8), NoReturn)  # type: ignore[arg-type]
+
+def test_absolute_reduce_invalid() -> None:
+    assert_type(np.absolute.reduce(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_frexp_reduce_invalid() -> None:
+    assert_type(np.frexp.reduce(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_divmod_reduce_invalid() -> None:
+    assert_type(np.divmod.reduce(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_matmul_reduce_invalid() -> None:
+    assert_type(np.matmul.reduce(AR_f8), NoReturn)  # type: ignore[arg-type]
+
+def test_absolute_accumulate_invalid() -> None:
+    assert_type(np.absolute.accumulate(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_frexp_accumulate_invalid() -> None:
+    assert_type(np.frexp.accumulate(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_divmod_accumulate_invalid() -> None:
+    assert_type(np.divmod.accumulate(AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_matmul_accumulate_invalid() -> None:
+    assert_type(np.matmul.accumulate(AR_f8), NoReturn)  # type: ignore[arg-type]
+
+def test_frexp_at_invalid() -> None:
+    assert_type(np.frexp.at(AR_f8, i8), NoReturn)  # type: ignore[arg-type]
+def test_divmod_at_invalid() -> None:
+    assert_type(np.divmod.at(AR_f8, i8, AR_f8), NoReturn)  # type: ignore[arg-type]
+def test_matmul_at_invalid() -> None:
+    assert_type(np.matmul.at(AR_f8, i8, AR_f8), NoReturn)  # type: ignore[arg-type]
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f756a8e45d46629d499612c5452932811dc95084
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/data/reveal/warnings_and_errors.pyi
@@ -0,0 +1,11 @@
+from typing import assert_type
+
+import numpy.exceptions as ex
+
+assert_type(ex.ModuleDeprecationWarning(), ex.ModuleDeprecationWarning)
+assert_type(ex.VisibleDeprecationWarning(), ex.VisibleDeprecationWarning)
+assert_type(ex.ComplexWarning(), ex.ComplexWarning)
+assert_type(ex.RankWarning(), ex.RankWarning)
+assert_type(ex.TooHardError(), ex.TooHardError)
+assert_type(ex.AxisError("test"), ex.AxisError)
+assert_type(ex.AxisError(5, 1), ex.AxisError)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_isfile.py b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_isfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e3157a1e54df6962c25b0b7a72a387b333156bb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_isfile.py
@@ -0,0 +1,38 @@
+import os
+import sys
+from pathlib import Path
+
+import pytest
+
+import numpy as np
+from numpy.testing import assert_
+
+ROOT = Path(np.__file__).parents[0]
+FILES = [
+    ROOT / "py.typed",
+    ROOT / "__init__.pyi",
+    ROOT / "ctypeslib" / "__init__.pyi",
+    ROOT / "_core" / "__init__.pyi",
+    ROOT / "f2py" / "__init__.pyi",
+    ROOT / "fft" / "__init__.pyi",
+    ROOT / "lib" / "__init__.pyi",
+    ROOT / "linalg" / "__init__.pyi",
+    ROOT / "ma" / "__init__.pyi",
+    ROOT / "matrixlib" / "__init__.pyi",
+    ROOT / "polynomial" / "__init__.pyi",
+    ROOT / "random" / "__init__.pyi",
+    ROOT / "testing" / "__init__.pyi",
+]
+if sys.version_info < (3, 12):
+    FILES += [ROOT / "distutils" / "__init__.pyi"]
+
+
+@pytest.mark.thread_unsafe(
+    reason="os.path has a thread-safety bug (python/cpython#140054). "
+           "Expected to only be a problem in 3.14.0"
+)
+class TestIsFile:
+    def test_isfile(self):
+        """Test if all ``.pyi`` files are properly installed."""
+        for file in FILES:
+            assert_(os.path.isfile(file))
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_runtime.py b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..462fe4eabdc024655807eb42394f0e244213500e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_runtime.py
@@ -0,0 +1,110 @@
+"""Test the runtime usage of `numpy.typing`."""
+
+from typing import (
+    Any,
+    NamedTuple,
+    Union,  # pyright: ignore[reportDeprecated]
+    get_args,
+    get_origin,
+    get_type_hints,
+)
+
+import pytest
+
+import numpy as np
+import numpy._typing as _npt
+import numpy.typing as npt
+
+
+class TypeTup(NamedTuple):
+    typ: type
+    args: tuple[type, ...]
+    origin: type | None
+
+
+def _flatten_type_alias(t: Any) -> Any:
+    # "flattens" a TypeAliasType to its underlying type alias
+    return getattr(t, "__value__", t)
+
+
+NDArrayTup = TypeTup(npt.NDArray, npt.NDArray.__args__, np.ndarray)
+
+TYPES = {
+    "ArrayLike": TypeTup(
+        _flatten_type_alias(npt.ArrayLike),
+        _flatten_type_alias(npt.ArrayLike).__args__,
+        Union,
+    ),
+    "DTypeLike": TypeTup(
+        _flatten_type_alias(npt.DTypeLike),
+        _flatten_type_alias(npt.DTypeLike).__args__,
+        Union,
+    ),
+    "NBitBase": TypeTup(npt.NBitBase, (), None),  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+    "NDArray": NDArrayTup,
+}
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_args(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_args`."""
+    typ, ref = tup.typ, tup.args
+    out = get_args(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_origin(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_origin`."""
+    typ, ref = tup.typ, tup.origin
+    out = get_origin(typ)
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints`."""
+    typ = tup.typ
+
+    def func(a: typ) -> None: pass
+
+    out = get_type_hints(func)
+    ref = {"a": typ, "return": type(None)}
+    assert out == ref
+
+
+@pytest.mark.parametrize("name,tup", TYPES.items(), ids=TYPES.keys())
+def test_get_type_hints_str(name: type, tup: TypeTup) -> None:
+    """Test `typing.get_type_hints` with string-representation of types."""
+    typ_str, typ = f"npt.{name}", tup.typ
+
+    def func(a: typ_str) -> None: pass
+
+    out = get_type_hints(func)
+    ref = {"a": getattr(npt, str(name)), "return": type(None)}
+    assert out == ref
+
+
+def test_keys() -> None:
+    """Test that ``TYPES.keys()`` and ``numpy.typing.__all__`` are synced."""
+    keys = TYPES.keys()
+    ref = set(npt.__all__)
+    assert keys == ref
+
+
+PROTOCOLS: dict[str, tuple[type[Any], object]] = {
+    "_SupportsArray": (_npt._SupportsArray, np.arange(10)),
+    "_SupportsArrayFunc": (_npt._SupportsArrayFunc, np.arange(10)),
+    "_NestedSequence": (_npt._NestedSequence, [1]),
+}
+
+
+@pytest.mark.parametrize("cls,obj", PROTOCOLS.values(), ids=PROTOCOLS.keys())
+class TestRuntimeProtocol:
+    def test_isinstance(self, cls: type[Any], obj: object) -> None:
+        assert isinstance(obj, cls)
+        assert not isinstance(None, cls)
+
+    def test_issubclass(self, cls: type[Any], obj: object) -> None:
+        assert issubclass(type(obj), cls)
+        assert not issubclass(type(None), cls)
diff --git a/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_typing.py b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca4cf37fec3b6e151b2396af6f1532cdf1dd49c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/typing/tests/test_typing.py
@@ -0,0 +1,205 @@
+import importlib.util
+import os
+import re
+import shutil
+import textwrap
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+import pytest
+
+# Only trigger a full `mypy` run if this environment variable is set
+# Note that these tests tend to take over a minute even on a macOS M1 CPU,
+# and more than that in CI.
+RUN_MYPY = "NPY_RUN_MYPY_IN_TESTSUITE" in os.environ
+if RUN_MYPY and RUN_MYPY not in ('0', '', 'false'):
+    RUN_MYPY = True
+
+# Skips all functions in this file
+pytestmark = pytest.mark.skipif(
+    not RUN_MYPY,
+    reason="`NPY_RUN_MYPY_IN_TESTSUITE` not set"
+)
+
+
+try:
+    from mypy import api
+except ImportError:
+    NO_MYPY = True
+else:
+    NO_MYPY = False
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+    # We need this as annotation, but it's located in a private namespace.
+    # As a compromise, do *not* import it during runtime
+    from _pytest.mark.structures import ParameterSet
+
+DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
+PASS_DIR = os.path.join(DATA_DIR, "pass")
+FAIL_DIR = os.path.join(DATA_DIR, "fail")
+REVEAL_DIR = os.path.join(DATA_DIR, "reveal")
+MISC_DIR = os.path.join(DATA_DIR, "misc")
+MYPY_INI = os.path.join(DATA_DIR, "mypy.ini")
+CACHE_DIR = os.path.join(DATA_DIR, ".mypy_cache")
+
+#: A dictionary with file names as keys and lists of the mypy stdout as values.
+#: To-be populated by `run_mypy`.
+OUTPUT_MYPY: defaultdict[str, list[str]] = defaultdict(list)
+
+
+def _key_func(key: str) -> str:
+    """Split at the first occurrence of the ``:`` character.
+
+    Windows drive-letters (*e.g.* ``C:``) are ignored herein.
+    """
+    drive, tail = os.path.splitdrive(key)
+    return os.path.join(drive, tail.split(":", 1)[0])
+
+
+def _strip_filename(msg: str) -> tuple[int, str]:
+    """Strip the filename and line number from a mypy message."""
+    _, tail = os.path.splitdrive(msg)
+    _, lineno, msg = tail.split(":", 2)
+    return int(lineno), msg.strip()
+
+
+def strip_func(match: re.Match[str]) -> str:
+    """`re.sub` helper function for stripping module names."""
+    return match.groups()[1]
+
+
+@pytest.fixture(scope="module", autouse=True)
+def run_mypy() -> None:
+    """Clears the cache and run mypy before running any of the typing tests.
+
+    The mypy results are cached in `OUTPUT_MYPY` for further use.
+
+    The cache refresh can be skipped using
+
+    NUMPY_TYPING_TEST_CLEAR_CACHE=0 pytest numpy/typing/tests
+    """
+    if (
+        os.path.isdir(CACHE_DIR)
+        and bool(os.environ.get("NUMPY_TYPING_TEST_CLEAR_CACHE", True))  # noqa: PLW1508
+    ):
+        shutil.rmtree(CACHE_DIR)
+
+    split_pattern = re.compile(r"(\s+)?\^(\~+)?")
+    for directory in (PASS_DIR, REVEAL_DIR, FAIL_DIR, MISC_DIR):
+        # Run mypy
+        stdout, stderr, exit_code = api.run([
+            "--config-file",
+            MYPY_INI,
+            "--cache-dir",
+            CACHE_DIR,
+            directory,
+        ])
+        if stderr:
+            pytest.fail(f"Unexpected mypy standard error\n\n{stderr}", False)
+        elif exit_code not in {0, 1}:
+            pytest.fail(f"Unexpected mypy exit code: {exit_code}\n\n{stdout}", False)
+
+        str_concat = ""
+        filename: str | None = None
+        for i in stdout.split("\n"):
+            if "note:" in i:
+                continue
+            if filename is None:
+                filename = _key_func(i)
+
+            str_concat += f"{i}\n"
+            if split_pattern.match(i) is not None:
+                OUTPUT_MYPY[filename].append(str_concat)
+                str_concat = ""
+                filename = None
+
+
+def get_test_cases(*directories: str) -> "Iterator[ParameterSet]":
+    for directory in directories:
+        for root, _, files in os.walk(directory):
+            for fname in files:
+                short_fname, ext = os.path.splitext(fname)
+                if ext not in (".pyi", ".py"):
+                    continue
+
+                fullpath = os.path.join(root, fname)
+                yield pytest.param(fullpath, id=short_fname)
+
+
+_FAIL_INDENT = " " * 4
+_FAIL_SEP = "\n" + "_" * 79 + "\n\n"
+
+_FAIL_MSG_REVEAL = """{}:{} - reveal mismatch:
+
+{}"""
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR, FAIL_DIR))
+def test_pass(path) -> None:
+    # Alias `OUTPUT_MYPY` so that it appears in the local namespace
+    output_mypy = OUTPUT_MYPY
+
+    if path not in output_mypy:
+        return
+
+    relpath = os.path.relpath(path)
+
+    # collect any reported errors, and clean up the output
+    messages = []
+    for message in output_mypy[path]:
+        lineno, content = _strip_filename(message)
+        content = content.removeprefix("error:").lstrip()
+        messages.append(f"{relpath}:{lineno} - {content}")
+
+    if messages:
+        pytest.fail("\n".join(messages), pytrace=False)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR))
+def test_reveal(path: str) -> None:
+    """Validate that mypy correctly infers the return-types of
+    the expressions in `path`.
+    """
+    __tracebackhide__ = True
+
+    output_mypy = OUTPUT_MYPY
+    if path not in output_mypy:
+        return
+
+    relpath = os.path.relpath(path)
+
+    # collect any reported errors, and clean up the output
+    failures = []
+    for error_line in output_mypy[path]:
+        lineno, error_msg = _strip_filename(error_line)
+        error_msg = textwrap.indent(error_msg, _FAIL_INDENT)
+        reason = _FAIL_MSG_REVEAL.format(relpath, lineno, error_msg)
+        failures.append(reason)
+
+    if failures:
+        reasons = _FAIL_SEP.join(failures)
+        pytest.fail(reasons, pytrace=False)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(NO_MYPY, reason="Mypy is not installed")
+@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
+def test_code_runs(path: str) -> None:
+    """Validate that the code in `path` properly during runtime."""
+    path_without_extension, _ = os.path.splitext(path)
+    dirname, filename = path.split(os.sep)[-2:]
+
+    spec = importlib.util.spec_from_file_location(
+        f"{dirname}.{filename}", path
+    )
+    assert spec is not None
+    assert spec.loader is not None
+
+    test_module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(test_module)
diff --git a/.venv/lib/python3.12/site-packages/numpy/version.py b/.venv/lib/python3.12/site-packages/numpy/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b901f6bf99d93c6b76fb05cc3236f8d97a8167fd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/version.py
@@ -0,0 +1,11 @@
+
+"""
+Module to expose more detailed version info for the installed `numpy`
+"""
+version = "2.4.3"
+__version__ = version
+full_version = version
+
+git_revision = "8bcb2e72e67c343e55165e6064fe6a9dc011e954"
+release = 'dev' not in version and '+' not in version
+short_version = version.split("+")[0]
diff --git a/.venv/lib/python3.12/site-packages/numpy/version.pyi b/.venv/lib/python3.12/site-packages/numpy/version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..073885c017c2a9c34e41431d0d9361de4b59ff90
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/numpy/version.pyi
@@ -0,0 +1,9 @@
+from typing import Final, LiteralString
+
+version: Final[LiteralString] = ...
+__version__: Final[LiteralString] = ...
+full_version: Final[LiteralString] = ...
+
+git_revision: Final[LiteralString] = ...
+release: Final[bool] = ...
+short_version: Final[LiteralString] = ...
diff --git a/.venv/lib/python3.12/site-packages/nvidia/__init__.py b/.venv/lib/python3.12/site-packages/nvidia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/nvidia/cublas/__init__.py b/.venv/lib/python3.12/site-packages/nvidia/cublas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/nvidia/cublas/include/__init__.py b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublas.h b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublas.h
new file mode 100644
index 0000000000000000000000000000000000000000..96eadad8a8e8c3979b99910ceea41ceaf2c8b58e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublas.h
@@ -0,0 +1,891 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ * This is the public header file for the CUBLAS library, defining the API
+ *
+ * CUBLAS is an implementation of BLAS (Basic Linear Algebra Subroutines)
+ * on top of the CUDA runtime.
+ */
+
+#if !defined(CUBLAS_H_)
+#define CUBLAS_H_
+
+#if defined(CUBLAS_V2_H_)
+#error "It is an error to include both cublas.h and cublas_v2.h"
+#endif
+
+#include <cuda_runtime.h>
+
+#ifndef CUBLASWINAPI
+#ifdef _WIN32
+#define CUBLASWINAPI __stdcall
+#else
+#define CUBLASWINAPI
+#endif
+#endif
+
+#undef CUBLASAPI
+#ifdef __CUDACC__
+#define CUBLASAPI __host__
+#else
+#define CUBLASAPI
+#endif
+
+#include "cublas_api.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* CUBLAS data types */
+#define cublasStatus cublasStatus_t
+
+cublasStatus CUBLASWINAPI cublasInit(void);
+cublasStatus CUBLASWINAPI cublasShutdown(void);
+cublasStatus CUBLASWINAPI cublasGetError(void);
+
+cublasStatus CUBLASWINAPI cublasGetVersion(int* version);
+cublasStatus CUBLASWINAPI cublasAlloc(int n, int elemSize, void** devicePtr);
+
+cublasStatus CUBLASWINAPI cublasFree(void* devicePtr);
+
+cublasStatus CUBLASWINAPI cublasSetKernelStream(cudaStream_t stream);
+
+/* ---------------- CUBLAS BLAS1 functions ---------------- */
+/* NRM2 */
+float CUBLASWINAPI cublasSnrm2(int n, const float* x, int incx);
+double CUBLASWINAPI cublasDnrm2(int n, const double* x, int incx);
+float CUBLASWINAPI cublasScnrm2(int n, const cuComplex* x, int incx);
+double CUBLASWINAPI cublasDznrm2(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* DOT */
+float CUBLASWINAPI cublasSdot(int n, const float* x, int incx, const float* y, int incy);
+double CUBLASWINAPI cublasDdot(int n, const double* x, int incx, const double* y, int incy);
+cuComplex CUBLASWINAPI cublasCdotu(int n, const cuComplex* x, int incx, const cuComplex* y, int incy);
+cuComplex CUBLASWINAPI cublasCdotc(int n, const cuComplex* x, int incx, const cuComplex* y, int incy);
+cuDoubleComplex CUBLASWINAPI cublasZdotu(int n, const cuDoubleComplex* x, int incx, const cuDoubleComplex* y, int incy);
+cuDoubleComplex CUBLASWINAPI cublasZdotc(int n, const cuDoubleComplex* x, int incx, const cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* SCAL */
+void CUBLASWINAPI cublasSscal(int n, float alpha, float* x, int incx);
+void CUBLASWINAPI cublasDscal(int n, double alpha, double* x, int incx);
+void CUBLASWINAPI cublasCscal(int n, cuComplex alpha, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZscal(int n, cuDoubleComplex alpha, cuDoubleComplex* x, int incx);
+
+void CUBLASWINAPI cublasCsscal(int n, float alpha, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZdscal(int n, double alpha, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* AXPY */
+void CUBLASWINAPI cublasSaxpy(int n, float alpha, const float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDaxpy(int n, double alpha, const double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCaxpy(int n, cuComplex alpha, const cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI
+cublasZaxpy(int n, cuDoubleComplex alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* COPY */
+void CUBLASWINAPI cublasScopy(int n, const float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDcopy(int n, const double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCcopy(int n, const cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI cublasZcopy(int n, const cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* SWAP */
+void CUBLASWINAPI cublasSswap(int n, float* x, int incx, float* y, int incy);
+void CUBLASWINAPI cublasDswap(int n, double* x, int incx, double* y, int incy);
+void CUBLASWINAPI cublasCswap(int n, cuComplex* x, int incx, cuComplex* y, int incy);
+void CUBLASWINAPI cublasZswap(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy);
+/*------------------------------------------------------------------------*/
+/* AMAX */
+int CUBLASWINAPI cublasIsamax(int n, const float* x, int incx);
+int CUBLASWINAPI cublasIdamax(int n, const double* x, int incx);
+int CUBLASWINAPI cublasIcamax(int n, const cuComplex* x, int incx);
+int CUBLASWINAPI cublasIzamax(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* AMIN */
+int CUBLASWINAPI cublasIsamin(int n, const float* x, int incx);
+int CUBLASWINAPI cublasIdamin(int n, const double* x, int incx);
+
+int CUBLASWINAPI cublasIcamin(int n, const cuComplex* x, int incx);
+int CUBLASWINAPI cublasIzamin(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* ASUM */
+float CUBLASWINAPI cublasSasum(int n, const float* x, int incx);
+double CUBLASWINAPI cublasDasum(int n, const double* x, int incx);
+float CUBLASWINAPI cublasScasum(int n, const cuComplex* x, int incx);
+double CUBLASWINAPI cublasDzasum(int n, const cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* ROT */
+void CUBLASWINAPI cublasSrot(int n, float* x, int incx, float* y, int incy, float sc, float ss);
+void CUBLASWINAPI cublasDrot(int n, double* x, int incx, double* y, int incy, double sc, double ss);
+void CUBLASWINAPI cublasCrot(int n, cuComplex* x, int incx, cuComplex* y, int incy, float c, cuComplex s);
+void CUBLASWINAPI
+cublasZrot(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy, double sc, cuDoubleComplex cs);
+void CUBLASWINAPI cublasCsrot(int n, cuComplex* x, int incx, cuComplex* y, int incy, float c, float s);
+void CUBLASWINAPI cublasZdrot(int n, cuDoubleComplex* x, int incx, cuDoubleComplex* y, int incy, double c, double s);
+/*------------------------------------------------------------------------*/
+/* ROTG */
+void CUBLASWINAPI cublasSrotg(float* sa, float* sb, float* sc, float* ss);
+void CUBLASWINAPI cublasDrotg(double* sa, double* sb, double* sc, double* ss);
+void CUBLASWINAPI cublasCrotg(cuComplex* ca, cuComplex cb, float* sc, cuComplex* cs);
+void CUBLASWINAPI cublasZrotg(cuDoubleComplex* ca, cuDoubleComplex cb, double* sc, cuDoubleComplex* cs);
+/*------------------------------------------------------------------------*/
+/* ROTM */
+void CUBLASWINAPI cublasSrotm(int n, float* x, int incx, float* y, int incy, const float* sparam);
+void CUBLASWINAPI cublasDrotm(int n, double* x, int incx, double* y, int incy, const double* sparam);
+/*------------------------------------------------------------------------*/
+/* ROTMG */
+void CUBLASWINAPI cublasSrotmg(float* sd1, float* sd2, float* sx1, const float* sy1, float* sparam);
+void CUBLASWINAPI cublasDrotmg(double* sd1, double* sd2, double* sx1, const double* sy1, double* sparam);
+
+/* --------------- CUBLAS BLAS2 functions  ---------------- */
+/* GEMV */
+void CUBLASWINAPI cublasSgemv(char trans,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDgemv(char trans,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasCgemv(char trans,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZgemv(char trans,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* GBMV */
+void CUBLASWINAPI cublasSgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasCgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZgbmv(char trans,
+                              int m,
+                              int n,
+                              int kl,
+                              int ku,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* TRMV */
+void CUBLASWINAPI cublasStrmv(char uplo, char trans, char diag, int n, const float* A, int lda, float* x, int incx);
+void CUBLASWINAPI cublasDtrmv(char uplo, char trans, char diag, int n, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtrmv(char uplo, char trans, char diag, int n, const cuComplex* A, int lda, cuComplex* x, int incx);
+void CUBLASWINAPI
+cublasZtrmv(char uplo, char trans, char diag, int n, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TBMV */
+void CUBLASWINAPI
+cublasStbmv(char uplo, char trans, char diag, int n, int k, const float* A, int lda, float* x, int incx);
+void CUBLASWINAPI
+cublasDtbmv(char uplo, char trans, char diag, int n, int k, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtbmv(char uplo, char trans, char diag, int n, int k, const cuComplex* A, int lda, cuComplex* x, int incx);
+void CUBLASWINAPI cublasZtbmv(
+    char uplo, char trans, char diag, int n, int k, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TPMV */
+void CUBLASWINAPI cublasStpmv(char uplo, char trans, char diag, int n, const float* AP, float* x, int incx);
+
+void CUBLASWINAPI cublasDtpmv(char uplo, char trans, char diag, int n, const double* AP, double* x, int incx);
+
+void CUBLASWINAPI cublasCtpmv(char uplo, char trans, char diag, int n, const cuComplex* AP, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtpmv(char uplo, char trans, char diag, int n, const cuDoubleComplex* AP, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TRSV */
+void CUBLASWINAPI cublasStrsv(char uplo, char trans, char diag, int n, const float* A, int lda, float* x, int incx);
+
+void CUBLASWINAPI cublasDtrsv(char uplo, char trans, char diag, int n, const double* A, int lda, double* x, int incx);
+
+void CUBLASWINAPI
+cublasCtrsv(char uplo, char trans, char diag, int n, const cuComplex* A, int lda, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtrsv(char uplo, char trans, char diag, int n, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TPSV */
+void CUBLASWINAPI cublasStpsv(char uplo, char trans, char diag, int n, const float* AP, float* x, int incx);
+
+void CUBLASWINAPI cublasDtpsv(char uplo, char trans, char diag, int n, const double* AP, double* x, int incx);
+
+void CUBLASWINAPI cublasCtpsv(char uplo, char trans, char diag, int n, const cuComplex* AP, cuComplex* x, int incx);
+
+void CUBLASWINAPI
+cublasZtpsv(char uplo, char trans, char diag, int n, const cuDoubleComplex* AP, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* TBSV */
+void CUBLASWINAPI
+cublasStbsv(char uplo, char trans, char diag, int n, int k, const float* A, int lda, float* x, int incx);
+
+void CUBLASWINAPI
+cublasDtbsv(char uplo, char trans, char diag, int n, int k, const double* A, int lda, double* x, int incx);
+void CUBLASWINAPI
+cublasCtbsv(char uplo, char trans, char diag, int n, int k, const cuComplex* A, int lda, cuComplex* x, int incx);
+
+void CUBLASWINAPI cublasZtbsv(
+    char uplo, char trans, char diag, int n, int k, const cuDoubleComplex* A, int lda, cuDoubleComplex* x, int incx);
+/*------------------------------------------------------------------------*/
+/* SYMV/HEMV */
+void CUBLASWINAPI cublasSsymv(
+    char uplo, int n, float alpha, const float* A, int lda, const float* x, int incx, float beta, float* y, int incy);
+void CUBLASWINAPI cublasDsymv(char uplo,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasChemv(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhemv(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* SBMV/HBMV */
+void CUBLASWINAPI cublasSsbmv(char uplo,
+                              int n,
+                              int k,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* x,
+                              int incx,
+                              float beta,
+                              float* y,
+                              int incy);
+void CUBLASWINAPI cublasDsbmv(char uplo,
+                              int n,
+                              int k,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* x,
+                              int incx,
+                              double beta,
+                              double* y,
+                              int incy);
+void CUBLASWINAPI cublasChbmv(char uplo,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhbmv(char uplo,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+/*------------------------------------------------------------------------*/
+/* SPMV/HPMV */
+void CUBLASWINAPI
+cublasSspmv(char uplo, int n, float alpha, const float* AP, const float* x, int incx, float beta, float* y, int incy);
+void CUBLASWINAPI cublasDspmv(
+    char uplo, int n, double alpha, const double* AP, const double* x, int incx, double beta, double* y, int incy);
+void CUBLASWINAPI cublasChpmv(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* AP,
+                              const cuComplex* x,
+                              int incx,
+                              cuComplex beta,
+                              cuComplex* y,
+                              int incy);
+void CUBLASWINAPI cublasZhpmv(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* AP,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* y,
+                              int incy);
+
+/*------------------------------------------------------------------------*/
+/* GER */
+void CUBLASWINAPI
+cublasSger(int m, int n, float alpha, const float* x, int incx, const float* y, int incy, float* A, int lda);
+void CUBLASWINAPI
+cublasDger(int m, int n, double alpha, const double* x, int incx, const double* y, int incy, double* A, int lda);
+
+void CUBLASWINAPI cublasCgeru(
+    int m, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* A, int lda);
+void CUBLASWINAPI cublasCgerc(
+    int m, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* A, int lda);
+void CUBLASWINAPI cublasZgeru(int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+void CUBLASWINAPI cublasZgerc(int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+/*------------------------------------------------------------------------*/
+/* SYR/HER */
+void CUBLASWINAPI cublasSsyr(char uplo, int n, float alpha, const float* x, int incx, float* A, int lda);
+void CUBLASWINAPI cublasDsyr(char uplo, int n, double alpha, const double* x, int incx, double* A, int lda);
+
+void CUBLASWINAPI cublasCher(char uplo, int n, float alpha, const cuComplex* x, int incx, cuComplex* A, int lda);
+void CUBLASWINAPI
+cublasZher(char uplo, int n, double alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* A, int lda);
+
+/*------------------------------------------------------------------------*/
+/* SPR/HPR */
+void CUBLASWINAPI cublasSspr(char uplo, int n, float alpha, const float* x, int incx, float* AP);
+void CUBLASWINAPI cublasDspr(char uplo, int n, double alpha, const double* x, int incx, double* AP);
+void CUBLASWINAPI cublasChpr(char uplo, int n, float alpha, const cuComplex* x, int incx, cuComplex* AP);
+void CUBLASWINAPI cublasZhpr(char uplo, int n, double alpha, const cuDoubleComplex* x, int incx, cuDoubleComplex* AP);
+/*------------------------------------------------------------------------*/
+/* SYR2/HER2 */
+void CUBLASWINAPI
+cublasSsyr2(char uplo, int n, float alpha, const float* x, int incx, const float* y, int incy, float* A, int lda);
+void CUBLASWINAPI
+cublasDsyr2(char uplo, int n, double alpha, const double* x, int incx, const double* y, int incy, double* A, int lda);
+void CUBLASWINAPI cublasCher2(char uplo,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* x,
+                              int incx,
+                              const cuComplex* y,
+                              int incy,
+                              cuComplex* A,
+                              int lda);
+void CUBLASWINAPI cublasZher2(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* A,
+                              int lda);
+
+/*------------------------------------------------------------------------*/
+/* SPR2/HPR2 */
+void CUBLASWINAPI
+cublasSspr2(char uplo, int n, float alpha, const float* x, int incx, const float* y, int incy, float* AP);
+void CUBLASWINAPI
+cublasDspr2(char uplo, int n, double alpha, const double* x, int incx, const double* y, int incy, double* AP);
+void CUBLASWINAPI cublasChpr2(
+    char uplo, int n, cuComplex alpha, const cuComplex* x, int incx, const cuComplex* y, int incy, cuComplex* AP);
+void CUBLASWINAPI cublasZhpr2(char uplo,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* x,
+                              int incx,
+                              const cuDoubleComplex* y,
+                              int incy,
+                              cuDoubleComplex* AP);
+/* ------------------------BLAS3 Functions ------------------------------- */
+/* GEMM */
+void CUBLASWINAPI cublasSgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* B,
+                              int ldb,
+                              float beta,
+                              float* C,
+                              int ldc);
+void CUBLASWINAPI cublasDgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* B,
+                              int ldb,
+                              double beta,
+                              double* C,
+                              int ldc);
+void CUBLASWINAPI cublasCgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZgemm(char transa,
+                              char transb,
+                              int m,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* -------------------------------------------------------*/
+/* SYRK */
+void CUBLASWINAPI
+cublasSsyrk(char uplo, char trans, int n, int k, float alpha, const float* A, int lda, float beta, float* C, int ldc);
+void CUBLASWINAPI cublasDsyrk(
+    char uplo, char trans, int n, int k, double alpha, const double* A, int lda, double beta, double* C, int ldc);
+
+void CUBLASWINAPI cublasCsyrk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZsyrk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* ------------------------------------------------------- */
+/* HERK */
+void CUBLASWINAPI cublasCherk(
+    char uplo, char trans, int n, int k, float alpha, const cuComplex* A, int lda, float beta, cuComplex* C, int ldc);
+void CUBLASWINAPI cublasZherk(char uplo,
+                              char trans,
+                              int n,
+                              int k,
+                              double alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              double beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/* ------------------------------------------------------- */
+/* SYR2K */
+void CUBLASWINAPI cublasSsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               float alpha,
+                               const float* A,
+                               int lda,
+                               const float* B,
+                               int ldb,
+                               float beta,
+                               float* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasDsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               double alpha,
+                               const double* A,
+                               int lda,
+                               const double* B,
+                               int ldb,
+                               double beta,
+                               double* C,
+                               int ldc);
+void CUBLASWINAPI cublasCsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuComplex alpha,
+                               const cuComplex* A,
+                               int lda,
+                               const cuComplex* B,
+                               int ldb,
+                               cuComplex beta,
+                               cuComplex* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasZsyr2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuDoubleComplex alpha,
+                               const cuDoubleComplex* A,
+                               int lda,
+                               const cuDoubleComplex* B,
+                               int ldb,
+                               cuDoubleComplex beta,
+                               cuDoubleComplex* C,
+                               int ldc);
+/* ------------------------------------------------------- */
+/* HER2K */
+void CUBLASWINAPI cublasCher2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuComplex alpha,
+                               const cuComplex* A,
+                               int lda,
+                               const cuComplex* B,
+                               int ldb,
+                               float beta,
+                               cuComplex* C,
+                               int ldc);
+
+void CUBLASWINAPI cublasZher2k(char uplo,
+                               char trans,
+                               int n,
+                               int k,
+                               cuDoubleComplex alpha,
+                               const cuDoubleComplex* A,
+                               int lda,
+                               const cuDoubleComplex* B,
+                               int ldb,
+                               double beta,
+                               cuDoubleComplex* C,
+                               int ldc);
+
+/*------------------------------------------------------------------------*/
+/* SYMM*/
+void CUBLASWINAPI cublasSsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              const float* B,
+                              int ldb,
+                              float beta,
+                              float* C,
+                              int ldc);
+void CUBLASWINAPI cublasDsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              const double* B,
+                              int ldb,
+                              double beta,
+                              double* C,
+                              int ldc);
+
+void CUBLASWINAPI cublasCsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+
+void CUBLASWINAPI cublasZsymm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+/*------------------------------------------------------------------------*/
+/* HEMM*/
+void CUBLASWINAPI cublasChemm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              const cuComplex* B,
+                              int ldb,
+                              cuComplex beta,
+                              cuComplex* C,
+                              int ldc);
+void CUBLASWINAPI cublasZhemm(char side,
+                              char uplo,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              const cuDoubleComplex* B,
+                              int ldb,
+                              cuDoubleComplex beta,
+                              cuDoubleComplex* C,
+                              int ldc);
+
+/*------------------------------------------------------------------------*/
+/* TRSM*/
+void CUBLASWINAPI cublasStrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              float* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasDtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              double* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasCtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex* B,
+                              int ldb);
+
+void CUBLASWINAPI cublasZtrsm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex* B,
+                              int ldb);
+/*------------------------------------------------------------------------*/
+/* TRMM*/
+void CUBLASWINAPI cublasStrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              float alpha,
+                              const float* A,
+                              int lda,
+                              float* B,
+                              int ldb);
+void CUBLASWINAPI cublasDtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              double alpha,
+                              const double* A,
+                              int lda,
+                              double* B,
+                              int ldb);
+void CUBLASWINAPI cublasCtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuComplex alpha,
+                              const cuComplex* A,
+                              int lda,
+                              cuComplex* B,
+                              int ldb);
+void CUBLASWINAPI cublasZtrmm(char side,
+                              char uplo,
+                              char transa,
+                              char diag,
+                              int m,
+                              int n,
+                              cuDoubleComplex alpha,
+                              const cuDoubleComplex* A,
+                              int lda,
+                              cuDoubleComplex* B,
+                              int ldb);
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(CUBLAS_H_) */
diff --git a/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasLt.h b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasLt.h
new file mode 100644
index 0000000000000000000000000000000000000000..217462f2ab4a16001f4ce6dc8c26a129d9f07b26
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasLt.h
@@ -0,0 +1,2511 @@
+/*
+ * Copyright 1993-2022 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+#pragma once
+
+#ifndef CUBLASAPI
+#ifdef __CUDACC__
+#define CUBLASAPI __host__ __device__
+#else
+#define CUBLASAPI
+#endif
+#endif
+
+#include <cublas_api.h>
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdio.h>
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+/** Opaque structure holding CUBLASLT context
+ */
+typedef struct cublasLtContext* cublasLtHandle_t;
+
+cublasStatus_t CUBLASWINAPI cublasLtCreate(cublasLtHandle_t* lightHandle);
+
+cublasStatus_t CUBLASWINAPI cublasLtDestroy(cublasLtHandle_t lightHandle);
+
+const char* CUBLASWINAPI cublasLtGetStatusName(cublasStatus_t status);
+
+const char* CUBLASWINAPI cublasLtGetStatusString(cublasStatus_t status);
+
+size_t CUBLASWINAPI cublasLtGetVersion(void);
+
+size_t CUBLASWINAPI cublasLtGetCudartVersion(void);
+
+cublasStatus_t CUBLASWINAPI cublasLtGetProperty(libraryPropertyType type, int* value);
+
+cublasStatus_t CUBLASWINAPI cublasLtHeuristicsCacheGetCapacity(size_t* capacity);
+cublasStatus_t CUBLASWINAPI cublasLtHeuristicsCacheSetCapacity(size_t capacity);
+
+/** Restricts usage of CPU instructions (ISA) specified by the flags in the mask.
+ *
+ * Flags can be combined with bitwise OR(|) operator. Supported flags:
+ * - 0x1 -- x86-64 AVX512 ISA
+ *
+ * Default mask: 0 (any applicable ISA is allowed).
+ *
+ * The function returns the previous value of the mask.
+ * The function takes precedence over the environment variable CUBLASLT_DISABLE_CPU_INSTRUCTIONS_MASK.
+ */
+unsigned CUBLASWINAPI cublasLtDisableCpuInstructionsSetMask(unsigned mask);
+
+/** Semi-opaque descriptor for matrix memory layout
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatrixLayoutOpaque_t;
+
+/** Opaque descriptor for matrix memory layout
+ */
+typedef cublasLtMatrixLayoutOpaque_t* cublasLtMatrixLayout_t;
+
+/** Semi-opaque algorithm descriptor (to avoid complicated alloc/free schemes)
+ *
+ * This structure can be trivially serialized and later restored for use with the same version of cuBLAS library to save
+ * on selecting the right configuration again.
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatmulAlgo_t;
+
+/** Semi-opaque descriptor for cublasLtMatmul() operation details
+ */
+typedef struct {
+  uint64_t data[32];
+} cublasLtMatmulDescOpaque_t;
+
+/** Opaque descriptor for cublasLtMatmul() operation details
+ */
+typedef cublasLtMatmulDescOpaque_t* cublasLtMatmulDesc_t;
+
+/** Semi-opaque descriptor for cublasLtMatrixTransform() operation details
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatrixTransformDescOpaque_t;
+
+/** Opaque descriptor for cublasLtMatrixTransform() operation details
+ */
+typedef cublasLtMatrixTransformDescOpaque_t* cublasLtMatrixTransformDesc_t;
+
+/** Semi-opaque descriptor for cublasLtMatmulPreference() operation details
+ */
+typedef struct {
+  uint64_t data[8];
+} cublasLtMatmulPreferenceOpaque_t;
+
+/** Opaque descriptor for cublasLtMatmulAlgoGetHeuristic() configuration
+ */
+typedef cublasLtMatmulPreferenceOpaque_t* cublasLtMatmulPreference_t;
+
+/** Tile size (in C/D matrix Rows x Cols)
+ *
+ * General order of tile IDs is sorted by size first and by first dimension second.
+ */
+typedef enum {
+  CUBLASLT_MATMUL_TILE_UNDEFINED = 0,
+  CUBLASLT_MATMUL_TILE_8x8 = 1,
+  CUBLASLT_MATMUL_TILE_8x16 = 2,
+  CUBLASLT_MATMUL_TILE_16x8 = 3,
+  CUBLASLT_MATMUL_TILE_8x32 = 4,
+  CUBLASLT_MATMUL_TILE_16x16 = 5,
+  CUBLASLT_MATMUL_TILE_32x8 = 6,
+  CUBLASLT_MATMUL_TILE_8x64 = 7,
+  CUBLASLT_MATMUL_TILE_16x32 = 8,
+  CUBLASLT_MATMUL_TILE_32x16 = 9,
+  CUBLASLT_MATMUL_TILE_64x8 = 10,
+  CUBLASLT_MATMUL_TILE_32x32 = 11,
+  CUBLASLT_MATMUL_TILE_32x64 = 12,
+  CUBLASLT_MATMUL_TILE_64x32 = 13,
+  CUBLASLT_MATMUL_TILE_32x128 = 14,
+  CUBLASLT_MATMUL_TILE_64x64 = 15,
+  CUBLASLT_MATMUL_TILE_128x32 = 16,
+  CUBLASLT_MATMUL_TILE_64x128 = 17,
+  CUBLASLT_MATMUL_TILE_128x64 = 18,
+  CUBLASLT_MATMUL_TILE_64x256 = 19,
+  CUBLASLT_MATMUL_TILE_128x128 = 20,
+  CUBLASLT_MATMUL_TILE_256x64 = 21,
+  CUBLASLT_MATMUL_TILE_64x512 = 22,
+  CUBLASLT_MATMUL_TILE_128x256 = 23,
+  CUBLASLT_MATMUL_TILE_256x128 = 24,
+  CUBLASLT_MATMUL_TILE_512x64 = 25,
+  CUBLASLT_MATMUL_TILE_64x96 = 26,
+  CUBLASLT_MATMUL_TILE_96x64 = 27,
+  CUBLASLT_MATMUL_TILE_96x128 = 28,
+  CUBLASLT_MATMUL_TILE_128x160 = 29,
+  CUBLASLT_MATMUL_TILE_160x128 = 30,
+  CUBLASLT_MATMUL_TILE_192x128 = 31,
+  CUBLASLT_MATMUL_TILE_128x192 = 32,
+  CUBLASLT_MATMUL_TILE_128x96 = 33,
+  CUBLASLT_MATMUL_TILE_32x256 = 34,
+  CUBLASLT_MATMUL_TILE_256x32 = 35,
+  CUBLASLT_MATMUL_TILE_8x128 = 36,
+  CUBLASLT_MATMUL_TILE_8x192 = 37,
+  CUBLASLT_MATMUL_TILE_8x256 = 38,
+  CUBLASLT_MATMUL_TILE_8x320 = 39,
+  CUBLASLT_MATMUL_TILE_8x384 = 40,
+  CUBLASLT_MATMUL_TILE_8x448 = 41,
+  CUBLASLT_MATMUL_TILE_8x512 = 42,
+  CUBLASLT_MATMUL_TILE_8x576 = 43,
+  CUBLASLT_MATMUL_TILE_8x640 = 44,
+  CUBLASLT_MATMUL_TILE_8x704 = 45,
+  CUBLASLT_MATMUL_TILE_8x768 = 46,
+  CUBLASLT_MATMUL_TILE_16x64 = 47,
+  CUBLASLT_MATMUL_TILE_16x128 = 48,
+  CUBLASLT_MATMUL_TILE_16x192 = 49,
+  CUBLASLT_MATMUL_TILE_16x256 = 50,
+  CUBLASLT_MATMUL_TILE_16x320 = 51,
+  CUBLASLT_MATMUL_TILE_16x384 = 52,
+  CUBLASLT_MATMUL_TILE_16x448 = 53,
+  CUBLASLT_MATMUL_TILE_16x512 = 54,
+  CUBLASLT_MATMUL_TILE_16x576 = 55,
+  CUBLASLT_MATMUL_TILE_16x640 = 56,
+  CUBLASLT_MATMUL_TILE_16x704 = 57,
+  CUBLASLT_MATMUL_TILE_16x768 = 58,
+  CUBLASLT_MATMUL_TILE_24x64 = 59,
+  CUBLASLT_MATMUL_TILE_24x128 = 60,
+  CUBLASLT_MATMUL_TILE_24x192 = 61,
+  CUBLASLT_MATMUL_TILE_24x256 = 62,
+  CUBLASLT_MATMUL_TILE_24x320 = 63,
+  CUBLASLT_MATMUL_TILE_24x384 = 64,
+  CUBLASLT_MATMUL_TILE_24x448 = 65,
+  CUBLASLT_MATMUL_TILE_24x512 = 66,
+  CUBLASLT_MATMUL_TILE_24x576 = 67,
+  CUBLASLT_MATMUL_TILE_24x640 = 68,
+  CUBLASLT_MATMUL_TILE_24x704 = 69,
+  CUBLASLT_MATMUL_TILE_24x768 = 70,
+  CUBLASLT_MATMUL_TILE_32x192 = 71,
+  CUBLASLT_MATMUL_TILE_32x320 = 72,
+  CUBLASLT_MATMUL_TILE_32x384 = 73,
+  CUBLASLT_MATMUL_TILE_32x448 = 74,
+  CUBLASLT_MATMUL_TILE_32x512 = 75,
+  CUBLASLT_MATMUL_TILE_32x576 = 76,
+  CUBLASLT_MATMUL_TILE_32x640 = 77,
+  CUBLASLT_MATMUL_TILE_32x704 = 78,
+  CUBLASLT_MATMUL_TILE_32x768 = 79,
+  CUBLASLT_MATMUL_TILE_40x64 = 80,
+  CUBLASLT_MATMUL_TILE_40x128 = 81,
+  CUBLASLT_MATMUL_TILE_40x192 = 82,
+  CUBLASLT_MATMUL_TILE_40x256 = 83,
+  CUBLASLT_MATMUL_TILE_40x320 = 84,
+  CUBLASLT_MATMUL_TILE_40x384 = 85,
+  CUBLASLT_MATMUL_TILE_40x448 = 86,
+  CUBLASLT_MATMUL_TILE_40x512 = 87,
+  CUBLASLT_MATMUL_TILE_40x576 = 88,
+  CUBLASLT_MATMUL_TILE_40x640 = 89,
+  CUBLASLT_MATMUL_TILE_40x704 = 90,
+  CUBLASLT_MATMUL_TILE_40x768 = 91,
+  CUBLASLT_MATMUL_TILE_48x64 = 92,
+  CUBLASLT_MATMUL_TILE_48x128 = 93,
+  CUBLASLT_MATMUL_TILE_48x192 = 94,
+  CUBLASLT_MATMUL_TILE_48x256 = 95,
+  CUBLASLT_MATMUL_TILE_48x320 = 96,
+  CUBLASLT_MATMUL_TILE_48x384 = 97,
+  CUBLASLT_MATMUL_TILE_48x448 = 98,
+  CUBLASLT_MATMUL_TILE_48x512 = 99,
+  CUBLASLT_MATMUL_TILE_48x576 = 100,
+  CUBLASLT_MATMUL_TILE_48x640 = 101,
+  CUBLASLT_MATMUL_TILE_48x704 = 102,
+  CUBLASLT_MATMUL_TILE_48x768 = 103,
+  CUBLASLT_MATMUL_TILE_56x64 = 104,
+  CUBLASLT_MATMUL_TILE_56x128 = 105,
+  CUBLASLT_MATMUL_TILE_56x192 = 106,
+  CUBLASLT_MATMUL_TILE_56x256 = 107,
+  CUBLASLT_MATMUL_TILE_56x320 = 108,
+  CUBLASLT_MATMUL_TILE_56x384 = 109,
+  CUBLASLT_MATMUL_TILE_56x448 = 110,
+  CUBLASLT_MATMUL_TILE_56x512 = 111,
+  CUBLASLT_MATMUL_TILE_56x576 = 112,
+  CUBLASLT_MATMUL_TILE_56x640 = 113,
+  CUBLASLT_MATMUL_TILE_56x704 = 114,
+  CUBLASLT_MATMUL_TILE_56x768 = 115,
+  CUBLASLT_MATMUL_TILE_64x192 = 116,
+  CUBLASLT_MATMUL_TILE_64x320 = 117,
+  CUBLASLT_MATMUL_TILE_64x384 = 118,
+  CUBLASLT_MATMUL_TILE_64x448 = 119,
+  CUBLASLT_MATMUL_TILE_64x576 = 120,
+  CUBLASLT_MATMUL_TILE_64x640 = 121,
+  CUBLASLT_MATMUL_TILE_64x704 = 122,
+  CUBLASLT_MATMUL_TILE_64x768 = 123,
+  CUBLASLT_MATMUL_TILE_72x64 = 124,
+  CUBLASLT_MATMUL_TILE_72x128 = 125,
+  CUBLASLT_MATMUL_TILE_72x192 = 126,
+  CUBLASLT_MATMUL_TILE_72x256 = 127,
+  CUBLASLT_MATMUL_TILE_72x320 = 128,
+  CUBLASLT_MATMUL_TILE_72x384 = 129,
+  CUBLASLT_MATMUL_TILE_72x448 = 130,
+  CUBLASLT_MATMUL_TILE_72x512 = 131,
+  CUBLASLT_MATMUL_TILE_72x576 = 132,
+  CUBLASLT_MATMUL_TILE_72x640 = 133,
+  CUBLASLT_MATMUL_TILE_80x64 = 134,
+  CUBLASLT_MATMUL_TILE_80x128 = 135,
+  CUBLASLT_MATMUL_TILE_80x192 = 136,
+  CUBLASLT_MATMUL_TILE_80x256 = 137,
+  CUBLASLT_MATMUL_TILE_80x320 = 138,
+  CUBLASLT_MATMUL_TILE_80x384 = 139,
+  CUBLASLT_MATMUL_TILE_80x448 = 140,
+  CUBLASLT_MATMUL_TILE_80x512 = 141,
+  CUBLASLT_MATMUL_TILE_80x576 = 142,
+  CUBLASLT_MATMUL_TILE_88x64 = 143,
+  CUBLASLT_MATMUL_TILE_88x128 = 144,
+  CUBLASLT_MATMUL_TILE_88x192 = 145,
+  CUBLASLT_MATMUL_TILE_88x256 = 146,
+  CUBLASLT_MATMUL_TILE_88x320 = 147,
+  CUBLASLT_MATMUL_TILE_88x384 = 148,
+  CUBLASLT_MATMUL_TILE_88x448 = 149,
+  CUBLASLT_MATMUL_TILE_88x512 = 150,
+  CUBLASLT_MATMUL_TILE_96x192 = 151,
+  CUBLASLT_MATMUL_TILE_96x256 = 152,
+  CUBLASLT_MATMUL_TILE_96x320 = 153,
+  CUBLASLT_MATMUL_TILE_96x384 = 154,
+  CUBLASLT_MATMUL_TILE_96x448 = 155,
+  CUBLASLT_MATMUL_TILE_96x512 = 156,
+  CUBLASLT_MATMUL_TILE_104x64 = 157,
+  CUBLASLT_MATMUL_TILE_104x128 = 158,
+  CUBLASLT_MATMUL_TILE_104x192 = 159,
+  CUBLASLT_MATMUL_TILE_104x256 = 160,
+  CUBLASLT_MATMUL_TILE_104x320 = 161,
+  CUBLASLT_MATMUL_TILE_104x384 = 162,
+  CUBLASLT_MATMUL_TILE_104x448 = 163,
+  CUBLASLT_MATMUL_TILE_112x64 = 164,
+  CUBLASLT_MATMUL_TILE_112x128 = 165,
+  CUBLASLT_MATMUL_TILE_112x192 = 166,
+  CUBLASLT_MATMUL_TILE_112x256 = 167,
+  CUBLASLT_MATMUL_TILE_112x320 = 168,
+  CUBLASLT_MATMUL_TILE_112x384 = 169,
+  CUBLASLT_MATMUL_TILE_120x64 = 170,
+  CUBLASLT_MATMUL_TILE_120x128 = 171,
+  CUBLASLT_MATMUL_TILE_120x192 = 172,
+  CUBLASLT_MATMUL_TILE_120x256 = 173,
+  CUBLASLT_MATMUL_TILE_120x320 = 174,
+  CUBLASLT_MATMUL_TILE_120x384 = 175,
+  CUBLASLT_MATMUL_TILE_128x320 = 176,
+  CUBLASLT_MATMUL_TILE_128x384 = 177,
+  CUBLASLT_MATMUL_TILE_136x64 = 178,
+  CUBLASLT_MATMUL_TILE_136x128 = 179,
+  CUBLASLT_MATMUL_TILE_136x192 = 180,
+  CUBLASLT_MATMUL_TILE_136x256 = 181,
+  CUBLASLT_MATMUL_TILE_136x320 = 182,
+  CUBLASLT_MATMUL_TILE_144x64 = 183,
+  CUBLASLT_MATMUL_TILE_144x128 = 184,
+  CUBLASLT_MATMUL_TILE_144x192 = 185,
+  CUBLASLT_MATMUL_TILE_144x256 = 186,
+  CUBLASLT_MATMUL_TILE_144x320 = 187,
+  CUBLASLT_MATMUL_TILE_152x64 = 188,
+  CUBLASLT_MATMUL_TILE_152x128 = 189,
+  CUBLASLT_MATMUL_TILE_152x192 = 190,
+  CUBLASLT_MATMUL_TILE_152x256 = 191,
+  CUBLASLT_MATMUL_TILE_152x320 = 192,
+  CUBLASLT_MATMUL_TILE_160x64 = 193,
+  CUBLASLT_MATMUL_TILE_160x192 = 194,
+  CUBLASLT_MATMUL_TILE_160x256 = 195,
+  CUBLASLT_MATMUL_TILE_168x64 = 196,
+  CUBLASLT_MATMUL_TILE_168x128 = 197,
+  CUBLASLT_MATMUL_TILE_168x192 = 198,
+  CUBLASLT_MATMUL_TILE_168x256 = 199,
+  CUBLASLT_MATMUL_TILE_176x64 = 200,
+  CUBLASLT_MATMUL_TILE_176x128 = 201,
+  CUBLASLT_MATMUL_TILE_176x192 = 202,
+  CUBLASLT_MATMUL_TILE_176x256 = 203,
+  CUBLASLT_MATMUL_TILE_184x64 = 204,
+  CUBLASLT_MATMUL_TILE_184x128 = 205,
+  CUBLASLT_MATMUL_TILE_184x192 = 206,
+  CUBLASLT_MATMUL_TILE_184x256 = 207,
+  CUBLASLT_MATMUL_TILE_192x64 = 208,
+  CUBLASLT_MATMUL_TILE_192x192 = 209,
+  CUBLASLT_MATMUL_TILE_192x256 = 210,
+  CUBLASLT_MATMUL_TILE_200x64 = 211,
+  CUBLASLT_MATMUL_TILE_200x128 = 212,
+  CUBLASLT_MATMUL_TILE_200x192 = 213,
+  CUBLASLT_MATMUL_TILE_208x64 = 214,
+  CUBLASLT_MATMUL_TILE_208x128 = 215,
+  CUBLASLT_MATMUL_TILE_208x192 = 216,
+  CUBLASLT_MATMUL_TILE_216x64 = 217,
+  CUBLASLT_MATMUL_TILE_216x128 = 218,
+  CUBLASLT_MATMUL_TILE_216x192 = 219,
+  CUBLASLT_MATMUL_TILE_224x64 = 220,
+  CUBLASLT_MATMUL_TILE_224x128 = 221,
+  CUBLASLT_MATMUL_TILE_224x192 = 222,
+  CUBLASLT_MATMUL_TILE_232x64 = 223,
+  CUBLASLT_MATMUL_TILE_232x128 = 224,
+  CUBLASLT_MATMUL_TILE_232x192 = 225,
+  CUBLASLT_MATMUL_TILE_240x64 = 226,
+  CUBLASLT_MATMUL_TILE_240x128 = 227,
+  CUBLASLT_MATMUL_TILE_240x192 = 228,
+  CUBLASLT_MATMUL_TILE_248x64 = 229,
+  CUBLASLT_MATMUL_TILE_248x128 = 230,
+  CUBLASLT_MATMUL_TILE_248x192 = 231,
+  CUBLASLT_MATMUL_TILE_256x192 = 232,
+  CUBLASLT_MATMUL_TILE_264x64 = 233,
+  CUBLASLT_MATMUL_TILE_264x128 = 234,
+  CUBLASLT_MATMUL_TILE_272x64 = 235,
+  CUBLASLT_MATMUL_TILE_272x128 = 236,
+  CUBLASLT_MATMUL_TILE_280x64 = 237,
+  CUBLASLT_MATMUL_TILE_280x128 = 238,
+  CUBLASLT_MATMUL_TILE_288x64 = 239,
+  CUBLASLT_MATMUL_TILE_288x128 = 240,
+  CUBLASLT_MATMUL_TILE_296x64 = 241,
+  CUBLASLT_MATMUL_TILE_296x128 = 242,
+  CUBLASLT_MATMUL_TILE_304x64 = 243,
+  CUBLASLT_MATMUL_TILE_304x128 = 244,
+  CUBLASLT_MATMUL_TILE_312x64 = 245,
+  CUBLASLT_MATMUL_TILE_312x128 = 246,
+  CUBLASLT_MATMUL_TILE_320x64 = 247,
+  CUBLASLT_MATMUL_TILE_320x128 = 248,
+  CUBLASLT_MATMUL_TILE_328x64 = 249,
+  CUBLASLT_MATMUL_TILE_328x128 = 250,
+  CUBLASLT_MATMUL_TILE_336x64 = 251,
+  CUBLASLT_MATMUL_TILE_336x128 = 252,
+  CUBLASLT_MATMUL_TILE_344x64 = 253,
+  CUBLASLT_MATMUL_TILE_344x128 = 254,
+  CUBLASLT_MATMUL_TILE_352x64 = 255,
+  CUBLASLT_MATMUL_TILE_352x128 = 256,
+  CUBLASLT_MATMUL_TILE_360x64 = 257,
+  CUBLASLT_MATMUL_TILE_360x128 = 258,
+  CUBLASLT_MATMUL_TILE_368x64 = 259,
+  CUBLASLT_MATMUL_TILE_368x128 = 260,
+  CUBLASLT_MATMUL_TILE_376x64 = 261,
+  CUBLASLT_MATMUL_TILE_376x128 = 262,
+  CUBLASLT_MATMUL_TILE_384x64 = 263,
+  CUBLASLT_MATMUL_TILE_384x128 = 264,
+  CUBLASLT_MATMUL_TILE_392x64 = 265,
+  CUBLASLT_MATMUL_TILE_400x64 = 266,
+  CUBLASLT_MATMUL_TILE_408x64 = 267,
+  CUBLASLT_MATMUL_TILE_416x64 = 268,
+  CUBLASLT_MATMUL_TILE_424x64 = 269,
+  CUBLASLT_MATMUL_TILE_432x64 = 270,
+  CUBLASLT_MATMUL_TILE_440x64 = 271,
+  CUBLASLT_MATMUL_TILE_448x64 = 272,
+  CUBLASLT_MATMUL_TILE_456x64 = 273,
+  CUBLASLT_MATMUL_TILE_464x64 = 274,
+  CUBLASLT_MATMUL_TILE_472x64 = 275,
+  CUBLASLT_MATMUL_TILE_480x64 = 276,
+  CUBLASLT_MATMUL_TILE_488x64 = 277,
+  CUBLASLT_MATMUL_TILE_496x64 = 278,
+  CUBLASLT_MATMUL_TILE_504x64 = 279,
+  CUBLASLT_MATMUL_TILE_520x64 = 280,
+  CUBLASLT_MATMUL_TILE_528x64 = 281,
+  CUBLASLT_MATMUL_TILE_536x64 = 282,
+  CUBLASLT_MATMUL_TILE_544x64 = 283,
+  CUBLASLT_MATMUL_TILE_552x64 = 284,
+  CUBLASLT_MATMUL_TILE_560x64 = 285,
+  CUBLASLT_MATMUL_TILE_568x64 = 286,
+  CUBLASLT_MATMUL_TILE_576x64 = 287,
+  CUBLASLT_MATMUL_TILE_584x64 = 288,
+  CUBLASLT_MATMUL_TILE_592x64 = 289,
+  CUBLASLT_MATMUL_TILE_600x64 = 290,
+  CUBLASLT_MATMUL_TILE_608x64 = 291,
+  CUBLASLT_MATMUL_TILE_616x64 = 292,
+  CUBLASLT_MATMUL_TILE_624x64 = 293,
+  CUBLASLT_MATMUL_TILE_632x64 = 294,
+  CUBLASLT_MATMUL_TILE_640x64 = 295,
+  CUBLASLT_MATMUL_TILE_648x64 = 296,
+  CUBLASLT_MATMUL_TILE_656x64 = 297,
+  CUBLASLT_MATMUL_TILE_664x64 = 298,
+  CUBLASLT_MATMUL_TILE_672x64 = 299,
+  CUBLASLT_MATMUL_TILE_680x64 = 300,
+  CUBLASLT_MATMUL_TILE_688x64 = 301,
+  CUBLASLT_MATMUL_TILE_696x64 = 302,
+  CUBLASLT_MATMUL_TILE_704x64 = 303,
+  CUBLASLT_MATMUL_TILE_712x64 = 304,
+  CUBLASLT_MATMUL_TILE_720x64 = 305,
+  CUBLASLT_MATMUL_TILE_728x64 = 306,
+  CUBLASLT_MATMUL_TILE_736x64 = 307,
+  CUBLASLT_MATMUL_TILE_744x64 = 308,
+  CUBLASLT_MATMUL_TILE_752x64 = 309,
+  CUBLASLT_MATMUL_TILE_760x64 = 310,
+  CUBLASLT_MATMUL_TILE_768x64 = 311,
+  CUBLASLT_MATMUL_TILE_64x16 = 312,
+  CUBLASLT_MATMUL_TILE_64x24 = 313,
+  CUBLASLT_MATMUL_TILE_64x40 = 314,
+  CUBLASLT_MATMUL_TILE_64x48 = 315,
+  CUBLASLT_MATMUL_TILE_64x56 = 316,
+  CUBLASLT_MATMUL_TILE_64x72 = 317,
+  CUBLASLT_MATMUL_TILE_64x80 = 318,
+  CUBLASLT_MATMUL_TILE_64x88 = 319,
+  CUBLASLT_MATMUL_TILE_64x104 = 320,
+  CUBLASLT_MATMUL_TILE_64x112 = 321,
+  CUBLASLT_MATMUL_TILE_64x120 = 322,
+  CUBLASLT_MATMUL_TILE_64x136 = 323,
+  CUBLASLT_MATMUL_TILE_64x144 = 324,
+  CUBLASLT_MATMUL_TILE_64x152 = 325,
+  CUBLASLT_MATMUL_TILE_64x160 = 326,
+  CUBLASLT_MATMUL_TILE_64x168 = 327,
+  CUBLASLT_MATMUL_TILE_64x176 = 328,
+  CUBLASLT_MATMUL_TILE_64x184 = 329,
+  CUBLASLT_MATMUL_TILE_64x200 = 330,
+  CUBLASLT_MATMUL_TILE_64x208 = 331,
+  CUBLASLT_MATMUL_TILE_64x216 = 332,
+  CUBLASLT_MATMUL_TILE_64x224 = 333,
+  CUBLASLT_MATMUL_TILE_64x232 = 334,
+  CUBLASLT_MATMUL_TILE_64x240 = 335,
+  CUBLASLT_MATMUL_TILE_64x248 = 336,
+  CUBLASLT_MATMUL_TILE_64x264 = 337,
+  CUBLASLT_MATMUL_TILE_64x272 = 338,
+  CUBLASLT_MATMUL_TILE_64x280 = 339,
+  CUBLASLT_MATMUL_TILE_64x288 = 340,
+  CUBLASLT_MATMUL_TILE_64x296 = 341,
+  CUBLASLT_MATMUL_TILE_64x304 = 342,
+  CUBLASLT_MATMUL_TILE_64x312 = 343,
+  CUBLASLT_MATMUL_TILE_64x328 = 344,
+  CUBLASLT_MATMUL_TILE_64x336 = 345,
+  CUBLASLT_MATMUL_TILE_64x344 = 346,
+  CUBLASLT_MATMUL_TILE_64x352 = 347,
+  CUBLASLT_MATMUL_TILE_64x360 = 348,
+  CUBLASLT_MATMUL_TILE_64x368 = 349,
+  CUBLASLT_MATMUL_TILE_64x376 = 350,
+  CUBLASLT_MATMUL_TILE_64x392 = 351,
+  CUBLASLT_MATMUL_TILE_64x400 = 352,
+  CUBLASLT_MATMUL_TILE_64x408 = 353,
+  CUBLASLT_MATMUL_TILE_64x416 = 354,
+  CUBLASLT_MATMUL_TILE_64x424 = 355,
+  CUBLASLT_MATMUL_TILE_64x432 = 356,
+  CUBLASLT_MATMUL_TILE_64x440 = 357,
+  CUBLASLT_MATMUL_TILE_64x456 = 358,
+  CUBLASLT_MATMUL_TILE_64x464 = 359,
+  CUBLASLT_MATMUL_TILE_64x472 = 360,
+  CUBLASLT_MATMUL_TILE_64x480 = 361,
+  CUBLASLT_MATMUL_TILE_64x488 = 362,
+  CUBLASLT_MATMUL_TILE_64x496 = 363,
+  CUBLASLT_MATMUL_TILE_64x504 = 364,
+  CUBLASLT_MATMUL_TILE_64x520 = 365,
+  CUBLASLT_MATMUL_TILE_64x528 = 366,
+  CUBLASLT_MATMUL_TILE_64x536 = 367,
+  CUBLASLT_MATMUL_TILE_64x544 = 368,
+  CUBLASLT_MATMUL_TILE_64x552 = 369,
+  CUBLASLT_MATMUL_TILE_64x560 = 370,
+  CUBLASLT_MATMUL_TILE_64x568 = 371,
+  CUBLASLT_MATMUL_TILE_64x584 = 372,
+  CUBLASLT_MATMUL_TILE_64x592 = 373,
+  CUBLASLT_MATMUL_TILE_64x600 = 374,
+  CUBLASLT_MATMUL_TILE_64x608 = 375,
+  CUBLASLT_MATMUL_TILE_64x616 = 376,
+  CUBLASLT_MATMUL_TILE_64x624 = 377,
+  CUBLASLT_MATMUL_TILE_64x632 = 378,
+  CUBLASLT_MATMUL_TILE_64x648 = 379,
+  CUBLASLT_MATMUL_TILE_64x656 = 380,
+  CUBLASLT_MATMUL_TILE_64x664 = 381,
+  CUBLASLT_MATMUL_TILE_64x672 = 382,
+  CUBLASLT_MATMUL_TILE_64x680 = 383,
+  CUBLASLT_MATMUL_TILE_64x688 = 384,
+  CUBLASLT_MATMUL_TILE_64x696 = 385,
+  CUBLASLT_MATMUL_TILE_64x712 = 386,
+  CUBLASLT_MATMUL_TILE_64x720 = 387,
+  CUBLASLT_MATMUL_TILE_64x728 = 388,
+  CUBLASLT_MATMUL_TILE_64x736 = 389,
+  CUBLASLT_MATMUL_TILE_64x744 = 390,
+  CUBLASLT_MATMUL_TILE_64x752 = 391,
+  CUBLASLT_MATMUL_TILE_64x760 = 392,
+  CUBLASLT_MATMUL_TILE_128x8 = 393,
+  CUBLASLT_MATMUL_TILE_128x16 = 394,
+  CUBLASLT_MATMUL_TILE_128x24 = 395,
+  CUBLASLT_MATMUL_TILE_128x40 = 396,
+  CUBLASLT_MATMUL_TILE_128x48 = 397,
+  CUBLASLT_MATMUL_TILE_128x56 = 398,
+  CUBLASLT_MATMUL_TILE_128x72 = 399,
+  CUBLASLT_MATMUL_TILE_128x80 = 400,
+  CUBLASLT_MATMUL_TILE_128x88 = 401,
+  CUBLASLT_MATMUL_TILE_128x104 = 402,
+  CUBLASLT_MATMUL_TILE_128x112 = 403,
+  CUBLASLT_MATMUL_TILE_128x120 = 404,
+  CUBLASLT_MATMUL_TILE_128x136 = 405,
+  CUBLASLT_MATMUL_TILE_128x144 = 406,
+  CUBLASLT_MATMUL_TILE_128x152 = 407,
+  CUBLASLT_MATMUL_TILE_128x168 = 408,
+  CUBLASLT_MATMUL_TILE_128x176 = 409,
+  CUBLASLT_MATMUL_TILE_128x184 = 410,
+  CUBLASLT_MATMUL_TILE_128x200 = 411,
+  CUBLASLT_MATMUL_TILE_128x208 = 412,
+  CUBLASLT_MATMUL_TILE_128x216 = 413,
+  CUBLASLT_MATMUL_TILE_128x224 = 414,
+  CUBLASLT_MATMUL_TILE_128x232 = 415,
+  CUBLASLT_MATMUL_TILE_128x240 = 416,
+  CUBLASLT_MATMUL_TILE_128x248 = 417,
+  CUBLASLT_MATMUL_TILE_128x264 = 418,
+  CUBLASLT_MATMUL_TILE_128x272 = 419,
+  CUBLASLT_MATMUL_TILE_128x280 = 420,
+  CUBLASLT_MATMUL_TILE_128x288 = 421,
+  CUBLASLT_MATMUL_TILE_128x296 = 422,
+  CUBLASLT_MATMUL_TILE_128x304 = 423,
+  CUBLASLT_MATMUL_TILE_128x312 = 424,
+  CUBLASLT_MATMUL_TILE_128x328 = 425,
+  CUBLASLT_MATMUL_TILE_128x336 = 426,
+  CUBLASLT_MATMUL_TILE_128x344 = 427,
+  CUBLASLT_MATMUL_TILE_128x352 = 428,
+  CUBLASLT_MATMUL_TILE_128x360 = 429,
+  CUBLASLT_MATMUL_TILE_128x368 = 430,
+  CUBLASLT_MATMUL_TILE_128x376 = 431,
+  CUBLASLT_MATMUL_TILE_128x392 = 432,
+  CUBLASLT_MATMUL_TILE_128x400 = 433,
+  CUBLASLT_MATMUL_TILE_128x408 = 434,
+  CUBLASLT_MATMUL_TILE_128x416 = 435,
+  CUBLASLT_MATMUL_TILE_128x424 = 436,
+  CUBLASLT_MATMUL_TILE_128x432 = 437,
+  CUBLASLT_MATMUL_TILE_128x440 = 438,
+  CUBLASLT_MATMUL_TILE_128x448 = 439,
+  CUBLASLT_MATMUL_TILE_128x456 = 440,
+  CUBLASLT_MATMUL_TILE_128x464 = 441,
+  CUBLASLT_MATMUL_TILE_128x472 = 442,
+  CUBLASLT_MATMUL_TILE_128x480 = 443,
+  CUBLASLT_MATMUL_TILE_128x488 = 444,
+  CUBLASLT_MATMUL_TILE_128x496 = 445,
+  CUBLASLT_MATMUL_TILE_128x504 = 446,
+  CUBLASLT_MATMUL_TILE_128x512 = 447,
+  CUBLASLT_MATMUL_TILE_192x8 = 448,
+  CUBLASLT_MATMUL_TILE_192x16 = 449,
+  CUBLASLT_MATMUL_TILE_192x24 = 450,
+  CUBLASLT_MATMUL_TILE_192x32 = 451,
+  CUBLASLT_MATMUL_TILE_192x40 = 452,
+  CUBLASLT_MATMUL_TILE_192x48 = 453,
+  CUBLASLT_MATMUL_TILE_192x56 = 454,
+  CUBLASLT_MATMUL_TILE_192x72 = 455,
+  CUBLASLT_MATMUL_TILE_192x80 = 456,
+  CUBLASLT_MATMUL_TILE_192x88 = 457,
+  CUBLASLT_MATMUL_TILE_192x96 = 458,
+  CUBLASLT_MATMUL_TILE_192x104 = 459,
+  CUBLASLT_MATMUL_TILE_192x112 = 460,
+  CUBLASLT_MATMUL_TILE_192x120 = 461,
+  CUBLASLT_MATMUL_TILE_192x136 = 462,
+  CUBLASLT_MATMUL_TILE_192x144 = 463,
+  CUBLASLT_MATMUL_TILE_192x152 = 464,
+  CUBLASLT_MATMUL_TILE_192x160 = 465,
+  CUBLASLT_MATMUL_TILE_192x168 = 466,
+  CUBLASLT_MATMUL_TILE_192x176 = 467,
+  CUBLASLT_MATMUL_TILE_192x184 = 468,
+  CUBLASLT_MATMUL_TILE_192x200 = 469,
+  CUBLASLT_MATMUL_TILE_192x208 = 470,
+  CUBLASLT_MATMUL_TILE_192x216 = 471,
+  CUBLASLT_MATMUL_TILE_192x224 = 472,
+  CUBLASLT_MATMUL_TILE_192x232 = 473,
+  CUBLASLT_MATMUL_TILE_192x240 = 474,
+  CUBLASLT_MATMUL_TILE_192x248 = 475,
+  CUBLASLT_MATMUL_TILE_192x264 = 476,
+  CUBLASLT_MATMUL_TILE_192x272 = 477,
+  CUBLASLT_MATMUL_TILE_192x280 = 478,
+  CUBLASLT_MATMUL_TILE_192x288 = 479,
+  CUBLASLT_MATMUL_TILE_192x296 = 480,
+  CUBLASLT_MATMUL_TILE_192x304 = 481,
+  CUBLASLT_MATMUL_TILE_192x312 = 482,
+  CUBLASLT_MATMUL_TILE_192x320 = 483,
+  CUBLASLT_MATMUL_TILE_192x328 = 484,
+  CUBLASLT_MATMUL_TILE_192x336 = 485,
+  CUBLASLT_MATMUL_TILE_256x8 = 486,
+  CUBLASLT_MATMUL_TILE_256x16 = 487,
+  CUBLASLT_MATMUL_TILE_256x24 = 488,
+  CUBLASLT_MATMUL_TILE_256x40 = 489,
+  CUBLASLT_MATMUL_TILE_256x48 = 490,
+  CUBLASLT_MATMUL_TILE_256x56 = 491,
+  CUBLASLT_MATMUL_TILE_256x72 = 492,
+  CUBLASLT_MATMUL_TILE_256x80 = 493,
+  CUBLASLT_MATMUL_TILE_256x88 = 494,
+  CUBLASLT_MATMUL_TILE_256x96 = 495,
+  CUBLASLT_MATMUL_TILE_256x104 = 496,
+  CUBLASLT_MATMUL_TILE_256x112 = 497,
+  CUBLASLT_MATMUL_TILE_256x120 = 498,
+  CUBLASLT_MATMUL_TILE_256x136 = 499,
+  CUBLASLT_MATMUL_TILE_256x144 = 500,
+  CUBLASLT_MATMUL_TILE_256x152 = 501,
+  CUBLASLT_MATMUL_TILE_256x160 = 502,
+  CUBLASLT_MATMUL_TILE_256x168 = 503,
+  CUBLASLT_MATMUL_TILE_256x176 = 504,
+  CUBLASLT_MATMUL_TILE_256x184 = 505,
+  CUBLASLT_MATMUL_TILE_256x200 = 506,
+  CUBLASLT_MATMUL_TILE_256x208 = 507,
+  CUBLASLT_MATMUL_TILE_256x216 = 508,
+  CUBLASLT_MATMUL_TILE_256x224 = 509,
+  CUBLASLT_MATMUL_TILE_256x232 = 510,
+  CUBLASLT_MATMUL_TILE_256x240 = 511,
+  CUBLASLT_MATMUL_TILE_256x248 = 512,
+  CUBLASLT_MATMUL_TILE_256x256 = 513,
+  CUBLASLT_MATMUL_TILE_320x8 = 514,
+  CUBLASLT_MATMUL_TILE_320x16 = 515,
+  CUBLASLT_MATMUL_TILE_320x24 = 516,
+  CUBLASLT_MATMUL_TILE_320x32 = 517,
+  CUBLASLT_MATMUL_TILE_320x40 = 518,
+  CUBLASLT_MATMUL_TILE_320x48 = 519,
+  CUBLASLT_MATMUL_TILE_320x56 = 520,
+  CUBLASLT_MATMUL_TILE_320x72 = 521,
+  CUBLASLT_MATMUL_TILE_320x80 = 522,
+  CUBLASLT_MATMUL_TILE_320x88 = 523,
+  CUBLASLT_MATMUL_TILE_320x96 = 524,
+  CUBLASLT_MATMUL_TILE_320x104 = 525,
+  CUBLASLT_MATMUL_TILE_320x112 = 526,
+  CUBLASLT_MATMUL_TILE_320x120 = 527,
+  CUBLASLT_MATMUL_TILE_320x136 = 528,
+  CUBLASLT_MATMUL_TILE_320x144 = 529,
+  CUBLASLT_MATMUL_TILE_320x152 = 530,
+  CUBLASLT_MATMUL_TILE_320x160 = 531,
+  CUBLASLT_MATMUL_TILE_320x168 = 532,
+  CUBLASLT_MATMUL_TILE_320x176 = 533,
+  CUBLASLT_MATMUL_TILE_320x184 = 534,
+  CUBLASLT_MATMUL_TILE_320x192 = 535,
+  CUBLASLT_MATMUL_TILE_320x200 = 536,
+  CUBLASLT_MATMUL_TILE_384x8 = 537,
+  CUBLASLT_MATMUL_TILE_384x16 = 538,
+  CUBLASLT_MATMUL_TILE_384x24 = 539,
+  CUBLASLT_MATMUL_TILE_384x32 = 540,
+  CUBLASLT_MATMUL_TILE_384x40 = 541,
+  CUBLASLT_MATMUL_TILE_384x48 = 542,
+  CUBLASLT_MATMUL_TILE_384x56 = 543,
+  CUBLASLT_MATMUL_TILE_384x72 = 544,
+  CUBLASLT_MATMUL_TILE_384x80 = 545,
+  CUBLASLT_MATMUL_TILE_384x88 = 546,
+  CUBLASLT_MATMUL_TILE_384x96 = 547,
+  CUBLASLT_MATMUL_TILE_384x104 = 548,
+  CUBLASLT_MATMUL_TILE_384x112 = 549,
+  CUBLASLT_MATMUL_TILE_384x120 = 550,
+  CUBLASLT_MATMUL_TILE_384x136 = 551,
+  CUBLASLT_MATMUL_TILE_384x144 = 552,
+  CUBLASLT_MATMUL_TILE_384x152 = 553,
+  CUBLASLT_MATMUL_TILE_384x160 = 554,
+  CUBLASLT_MATMUL_TILE_384x168 = 555,
+  CUBLASLT_MATMUL_TILE_448x8 = 556,
+  CUBLASLT_MATMUL_TILE_448x16 = 557,
+  CUBLASLT_MATMUL_TILE_448x24 = 558,
+  CUBLASLT_MATMUL_TILE_448x32 = 559,
+  CUBLASLT_MATMUL_TILE_448x40 = 560,
+  CUBLASLT_MATMUL_TILE_448x48 = 561,
+  CUBLASLT_MATMUL_TILE_448x56 = 562,
+  CUBLASLT_MATMUL_TILE_448x72 = 563,
+  CUBLASLT_MATMUL_TILE_448x80 = 564,
+  CUBLASLT_MATMUL_TILE_448x88 = 565,
+  CUBLASLT_MATMUL_TILE_448x96 = 566,
+  CUBLASLT_MATMUL_TILE_448x104 = 567,
+  CUBLASLT_MATMUL_TILE_448x112 = 568,
+  CUBLASLT_MATMUL_TILE_448x120 = 569,
+  CUBLASLT_MATMUL_TILE_448x128 = 570,
+  CUBLASLT_MATMUL_TILE_448x136 = 571,
+  CUBLASLT_MATMUL_TILE_448x144 = 572,
+  CUBLASLT_MATMUL_TILE_512x8 = 573,
+  CUBLASLT_MATMUL_TILE_512x16 = 574,
+  CUBLASLT_MATMUL_TILE_512x24 = 575,
+  CUBLASLT_MATMUL_TILE_512x32 = 576,
+  CUBLASLT_MATMUL_TILE_512x40 = 577,
+  CUBLASLT_MATMUL_TILE_512x48 = 578,
+  CUBLASLT_MATMUL_TILE_512x56 = 579,
+  CUBLASLT_MATMUL_TILE_512x72 = 580,
+  CUBLASLT_MATMUL_TILE_512x80 = 581,
+  CUBLASLT_MATMUL_TILE_512x88 = 582,
+  CUBLASLT_MATMUL_TILE_512x96 = 583,
+  CUBLASLT_MATMUL_TILE_512x104 = 584,
+  CUBLASLT_MATMUL_TILE_512x112 = 585,
+  CUBLASLT_MATMUL_TILE_512x120 = 586,
+  CUBLASLT_MATMUL_TILE_512x128 = 587,
+  CUBLASLT_MATMUL_TILE_576x8 = 588,
+  CUBLASLT_MATMUL_TILE_576x16 = 589,
+  CUBLASLT_MATMUL_TILE_576x24 = 590,
+  CUBLASLT_MATMUL_TILE_576x32 = 591,
+  CUBLASLT_MATMUL_TILE_576x40 = 592,
+  CUBLASLT_MATMUL_TILE_576x48 = 593,
+  CUBLASLT_MATMUL_TILE_576x56 = 594,
+  CUBLASLT_MATMUL_TILE_576x72 = 595,
+  CUBLASLT_MATMUL_TILE_576x80 = 596,
+  CUBLASLT_MATMUL_TILE_576x88 = 597,
+  CUBLASLT_MATMUL_TILE_576x96 = 598,
+  CUBLASLT_MATMUL_TILE_576x104 = 599,
+  CUBLASLT_MATMUL_TILE_576x112 = 600,
+  CUBLASLT_MATMUL_TILE_640x8 = 601,
+  CUBLASLT_MATMUL_TILE_640x16 = 602,
+  CUBLASLT_MATMUL_TILE_640x24 = 603,
+  CUBLASLT_MATMUL_TILE_640x32 = 604,
+  CUBLASLT_MATMUL_TILE_640x40 = 605,
+  CUBLASLT_MATMUL_TILE_640x48 = 606,
+  CUBLASLT_MATMUL_TILE_640x56 = 607,
+  CUBLASLT_MATMUL_TILE_640x72 = 608,
+  CUBLASLT_MATMUL_TILE_640x80 = 609,
+  CUBLASLT_MATMUL_TILE_640x88 = 610,
+  CUBLASLT_MATMUL_TILE_640x96 = 611,
+  CUBLASLT_MATMUL_TILE_704x8 = 612,
+  CUBLASLT_MATMUL_TILE_704x16 = 613,
+  CUBLASLT_MATMUL_TILE_704x24 = 614,
+  CUBLASLT_MATMUL_TILE_704x32 = 615,
+  CUBLASLT_MATMUL_TILE_704x40 = 616,
+  CUBLASLT_MATMUL_TILE_704x48 = 617,
+  CUBLASLT_MATMUL_TILE_704x56 = 618,
+  CUBLASLT_MATMUL_TILE_704x72 = 619,
+  CUBLASLT_MATMUL_TILE_704x80 = 620,
+  CUBLASLT_MATMUL_TILE_704x88 = 621,
+  CUBLASLT_MATMUL_TILE_768x8 = 622,
+  CUBLASLT_MATMUL_TILE_768x16 = 623,
+  CUBLASLT_MATMUL_TILE_768x24 = 624,
+  CUBLASLT_MATMUL_TILE_768x32 = 625,
+  CUBLASLT_MATMUL_TILE_768x40 = 626,
+  CUBLASLT_MATMUL_TILE_768x48 = 627,
+  CUBLASLT_MATMUL_TILE_768x56 = 628,
+  CUBLASLT_MATMUL_TILE_768x72 = 629,
+  CUBLASLT_MATMUL_TILE_768x80 = 630,
+  CUBLASLT_MATMUL_TILE_256x512 = 631,
+  CUBLASLT_MATMUL_TILE_256x1024 = 632,
+  CUBLASLT_MATMUL_TILE_512x512 = 633,
+  CUBLASLT_MATMUL_TILE_512x1024 = 634,
+  CUBLASLT_MATMUL_TILE_END
+} cublasLtMatmulTile_t;
+
+/** Size and number of stages in which elements are read into shared memory
+ *
+ * General order of stages IDs is sorted by stage size first and by number of stages second.
+ */
+typedef enum {
+  CUBLASLT_MATMUL_STAGES_UNDEFINED = 0,
+  CUBLASLT_MATMUL_STAGES_16x1 = 1,
+  CUBLASLT_MATMUL_STAGES_16x2 = 2,
+  CUBLASLT_MATMUL_STAGES_16x3 = 3,
+  CUBLASLT_MATMUL_STAGES_16x4 = 4,
+  CUBLASLT_MATMUL_STAGES_16x5 = 5,
+  CUBLASLT_MATMUL_STAGES_16x6 = 6,
+  CUBLASLT_MATMUL_STAGES_32x1 = 7,
+  CUBLASLT_MATMUL_STAGES_32x2 = 8,
+  CUBLASLT_MATMUL_STAGES_32x3 = 9,
+  CUBLASLT_MATMUL_STAGES_32x4 = 10,
+  CUBLASLT_MATMUL_STAGES_32x5 = 11,
+  CUBLASLT_MATMUL_STAGES_32x6 = 12,
+  CUBLASLT_MATMUL_STAGES_64x1 = 13,
+  CUBLASLT_MATMUL_STAGES_64x2 = 14,
+  CUBLASLT_MATMUL_STAGES_64x3 = 15,
+  CUBLASLT_MATMUL_STAGES_64x4 = 16,
+  CUBLASLT_MATMUL_STAGES_64x5 = 17,
+  CUBLASLT_MATMUL_STAGES_64x6 = 18,
+  CUBLASLT_MATMUL_STAGES_128x1 = 19,
+  CUBLASLT_MATMUL_STAGES_128x2 = 20,
+  CUBLASLT_MATMUL_STAGES_128x3 = 21,
+  CUBLASLT_MATMUL_STAGES_128x4 = 22,
+  CUBLASLT_MATMUL_STAGES_128x5 = 23,
+  CUBLASLT_MATMUL_STAGES_128x6 = 24,
+  CUBLASLT_MATMUL_STAGES_32x10 = 25,
+  CUBLASLT_MATMUL_STAGES_8x4 = 26,
+  CUBLASLT_MATMUL_STAGES_16x10 = 27,
+  CUBLASLT_MATMUL_STAGES_8x5 = 28,
+  CUBLASLT_MATMUL_STAGES_8x3 = 31,
+  CUBLASLT_MATMUL_STAGES_8xAUTO = 32,
+  CUBLASLT_MATMUL_STAGES_16xAUTO = 33,
+  CUBLASLT_MATMUL_STAGES_32xAUTO = 34,
+  CUBLASLT_MATMUL_STAGES_64xAUTO = 35,
+  CUBLASLT_MATMUL_STAGES_128xAUTO = 36,
+  CUBLASLT_MATMUL_STAGES_256xAUTO = 37,
+  CUBLASLT_MATMUL_STAGES_END
+} cublasLtMatmulStages_t;
+
+/** Thread Block Cluster size
+ *
+ * Typically dimensioned similar to cublasLtMatmulTile_t, with the third coordinate unused at this time.
+ */
+typedef enum {
+  /** Let library pick cluster shape automatically */
+  CUBLASLT_CLUSTER_SHAPE_AUTO = 0,
+  CUBLASLT_CLUSTER_SHAPE_1x1x1 = 2,
+  CUBLASLT_CLUSTER_SHAPE_2x1x1 = 3,
+  CUBLASLT_CLUSTER_SHAPE_4x1x1 = 4,
+  CUBLASLT_CLUSTER_SHAPE_1x2x1 = 5,
+  CUBLASLT_CLUSTER_SHAPE_2x2x1 = 6,
+  CUBLASLT_CLUSTER_SHAPE_4x2x1 = 7,
+  CUBLASLT_CLUSTER_SHAPE_1x4x1 = 8,
+  CUBLASLT_CLUSTER_SHAPE_2x4x1 = 9,
+  CUBLASLT_CLUSTER_SHAPE_4x4x1 = 10,
+  CUBLASLT_CLUSTER_SHAPE_8x1x1 = 11,
+  CUBLASLT_CLUSTER_SHAPE_1x8x1 = 12,
+  CUBLASLT_CLUSTER_SHAPE_8x2x1 = 13,
+  CUBLASLT_CLUSTER_SHAPE_2x8x1 = 14,
+  CUBLASLT_CLUSTER_SHAPE_16x1x1 = 15,
+  CUBLASLT_CLUSTER_SHAPE_1x16x1 = 16,
+  CUBLASLT_CLUSTER_SHAPE_3x1x1 = 17,
+  CUBLASLT_CLUSTER_SHAPE_5x1x1 = 18,
+  CUBLASLT_CLUSTER_SHAPE_6x1x1 = 19,
+  CUBLASLT_CLUSTER_SHAPE_7x1x1 = 20,
+  CUBLASLT_CLUSTER_SHAPE_9x1x1 = 21,
+  CUBLASLT_CLUSTER_SHAPE_10x1x1 = 22,
+  CUBLASLT_CLUSTER_SHAPE_11x1x1 = 23,
+  CUBLASLT_CLUSTER_SHAPE_12x1x1 = 24,
+  CUBLASLT_CLUSTER_SHAPE_13x1x1 = 25,
+  CUBLASLT_CLUSTER_SHAPE_14x1x1 = 26,
+  CUBLASLT_CLUSTER_SHAPE_15x1x1 = 27,
+  CUBLASLT_CLUSTER_SHAPE_3x2x1 = 28,
+  CUBLASLT_CLUSTER_SHAPE_5x2x1 = 29,
+  CUBLASLT_CLUSTER_SHAPE_6x2x1 = 30,
+  CUBLASLT_CLUSTER_SHAPE_7x2x1 = 31,
+  CUBLASLT_CLUSTER_SHAPE_1x3x1 = 32,
+  CUBLASLT_CLUSTER_SHAPE_2x3x1 = 33,
+  CUBLASLT_CLUSTER_SHAPE_3x3x1 = 34,
+  CUBLASLT_CLUSTER_SHAPE_4x3x1 = 35,
+  CUBLASLT_CLUSTER_SHAPE_5x3x1 = 36,
+  CUBLASLT_CLUSTER_SHAPE_3x4x1 = 37,
+  CUBLASLT_CLUSTER_SHAPE_1x5x1 = 38,
+  CUBLASLT_CLUSTER_SHAPE_2x5x1 = 39,
+  CUBLASLT_CLUSTER_SHAPE_3x5x1 = 40,
+  CUBLASLT_CLUSTER_SHAPE_1x6x1 = 41,
+  CUBLASLT_CLUSTER_SHAPE_2x6x1 = 42,
+  CUBLASLT_CLUSTER_SHAPE_1x7x1 = 43,
+  CUBLASLT_CLUSTER_SHAPE_2x7x1 = 44,
+  CUBLASLT_CLUSTER_SHAPE_1x9x1 = 45,
+  CUBLASLT_CLUSTER_SHAPE_1x10x1 = 46,
+  CUBLASLT_CLUSTER_SHAPE_1x11x1 = 47,
+  CUBLASLT_CLUSTER_SHAPE_1x12x1 = 48,
+  CUBLASLT_CLUSTER_SHAPE_1x13x1 = 49,
+  CUBLASLT_CLUSTER_SHAPE_1x14x1 = 50,
+  CUBLASLT_CLUSTER_SHAPE_1x15x1 = 51,
+  CUBLASLT_CLUSTER_SHAPE_END
+} cublasLtClusterShape_t;
+
+/** Inner size of the kernel
+ *
+ * Represents various aspects of internal kernel design, that don't impact CUDA grid size but may have other more subtle
+ * effects.
+ *
+ */
+typedef enum {
+  CUBLASLT_MATMUL_INNER_SHAPE_UNDEFINED = 0,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA884 = 1,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA1684 = 2,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA1688 = 3,
+  CUBLASLT_MATMUL_INNER_SHAPE_MMA16816 = 4,
+  CUBLASLT_MATMUL_INNER_SHAPE_END
+} cublasLtMatmulInnerShape_t;
+
+/** Scaling mode for per-matrix scaling */
+typedef enum {
+  /** Scaling factors are single precision scalars applied to the whole tensor */
+  CUBLASLT_MATMUL_MATRIX_SCALE_SCALAR_32F = 0,
+  /** Scaling factors are tensors that contain a dedicated scaling factor stored as an 8-bit CUDA_R_8F_UE4M3 value for
+     each 16-element block in the innermost dimension of the corresponding data tensor */
+  CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3 = 1,
+  /** Same as above, except that scaling factor tensor elements have type CUDA_R_8F_UE8M0 and the block size is 32
+     elements*/
+  CUBLASLT_MATMUL_MATRIX_SCALE_VEC32_UE8M0 = 2,
+  CUBLASLT_MATMUL_MATRIX_SCALE_END
+} cublasLtMatmulMatrixScale_t;
+
+/** Pointer mode to use for alpha/beta */
+typedef enum {
+  /** matches CUBLAS_POINTER_MODE_HOST, pointer targets a single value host memory */
+  CUBLASLT_POINTER_MODE_HOST = CUBLAS_POINTER_MODE_HOST,
+  /** matches CUBLAS_POINTER_MODE_DEVICE, pointer targets a single value device memory */
+  CUBLASLT_POINTER_MODE_DEVICE = CUBLAS_POINTER_MODE_DEVICE,
+  /** pointer targets an array in device memory */
+  CUBLASLT_POINTER_MODE_DEVICE_VECTOR = 2,
+  /** alpha pointer targets an array in device memory, beta is zero. Note:
+     CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE is not supported, must be 0. */
+  CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO = 3,
+  /** alpha pointer targets an array in device memory, beta is a single value in host memory. */
+  CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST = 4,
+} cublasLtPointerMode_t;
+
+/** Mask to define pointer mode capability */
+typedef enum {
+  /** see CUBLASLT_POINTER_MODE_HOST */
+  CUBLASLT_POINTER_MODE_MASK_HOST = 1,
+  /** see CUBLASLT_POINTER_MODE_DEVICE */
+  CUBLASLT_POINTER_MODE_MASK_DEVICE = 2,
+  /** see CUBLASLT_POINTER_MODE_DEVICE_VECTOR */
+  CUBLASLT_POINTER_MODE_MASK_DEVICE_VECTOR = 4,
+  /** see CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO */
+  CUBLASLT_POINTER_MODE_MASK_ALPHA_DEVICE_VECTOR_BETA_ZERO = 8,
+  /** see CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST */
+  CUBLASLT_POINTER_MODE_MASK_ALPHA_DEVICE_VECTOR_BETA_HOST = 16,
+} cublasLtPointerModeMask_t;
+
+/** Implementation details that may affect numerical behavior of algorithms. */
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_FMA (0x01ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_HMMA (0x02ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_IMMA (0x04ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_DMMA (0x08ull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_TENSOR_OP_MASK (0xfeull << 0)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_OP_TYPE_MASK (0xffull << 0)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_16F (0x01ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_32F (0x02ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_64F (0x04ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_32I (0x08ull << 8)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_ACCUMULATOR_TYPE_MASK (0xffull << 8)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_16F (0x01ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_16BF (0x02ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_TF32 (0x04ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_32F (0x08ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_64F (0x10ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8I (0x20ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8F_E4M3 (0x40ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_INPUT_8F_E5M2 (0x80ull << 16)
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_OP_INPUT_TYPE_MASK (0xffull << 16)
+
+#define CUBLASLT_NUMERICAL_IMPL_FLAGS_GAUSSIAN (0x01ull << 32)
+typedef uint64_t cublasLtNumericalImplFlags_t;
+
+/** Execute matrix multiplication (D = alpha * op(A) * op(B) + beta * C).
+ *
+ * \retval     CUBLAS_STATUS_NOT_INITIALIZED   if cuBLASLt handle has not been initialized
+ * \retval     CUBLAS_STATUS_INVALID_VALUE     if parameters are in conflict or in an impossible configuration; e.g.
+ *                                             when workspaceSizeInBytes is less than workspace required by configured
+ *                                             algo
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED     if current implementation on selected device doesn't support configured
+ *                                             operation
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH     if configured operation cannot be run using selected device
+ * \retval     CUBLAS_STATUS_EXECUTION_FAILED  if cuda reported execution error from the device
+ * \retval     CUBLAS_STATUS_SUCCESS           if the operation completed successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmul(cublasLtHandle_t lightHandle,
+                                           cublasLtMatmulDesc_t computeDesc,
+                                           const void* alpha, /* host or device pointer */
+                                           const void* A,
+                                           cublasLtMatrixLayout_t Adesc,
+                                           const void* B,
+                                           cublasLtMatrixLayout_t Bdesc,
+                                           const void* beta, /* host or device pointer */
+                                           const void* C,
+                                           cublasLtMatrixLayout_t Cdesc,
+                                           void* D,
+                                           cublasLtMatrixLayout_t Ddesc,
+                                           const cublasLtMatmulAlgo_t* algo,
+                                           void* workspace,
+                                           size_t workspaceSizeInBytes,
+                                           cudaStream_t stream);
+
+/** Matrix layout conversion helper (C = alpha * op(A) + beta * op(B))
+ *
+ * Can be used to change memory order of data or to scale and shift the values.
+ *
+ * \retval     CUBLAS_STATUS_NOT_INITIALIZED   if cuBLASLt handle has not been initialized
+ * \retval     CUBLAS_STATUS_INVALID_VALUE     if parameters are in conflict or in an impossible configuration; e.g.
+ *                                             when A is not NULL, but Adesc is NULL
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED     if current implementation on selected device doesn't support configured
+ *                                             operation
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH     if configured operation cannot be run using selected device
+ * \retval     CUBLAS_STATUS_EXECUTION_FAILED  if cuda reported execution error from the device
+ * \retval     CUBLAS_STATUS_SUCCESS           if the operation completed successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransform(cublasLtHandle_t lightHandle,
+                                                    cublasLtMatrixTransformDesc_t transformDesc,
+                                                    const void* alpha, /* host or device pointer */
+                                                    const void* A,
+                                                    cublasLtMatrixLayout_t Adesc,
+                                                    const void* beta, /* host or device pointer */
+                                                    const void* B,
+                                                    cublasLtMatrixLayout_t Bdesc,
+                                                    void* C,
+                                                    cublasLtMatrixLayout_t Cdesc,
+                                                    cudaStream_t stream);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatrixLayout_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Enum for data ordering */
+typedef enum {
+  /** Column-major
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next column in memory.
+   */
+  CUBLASLT_ORDER_COL = 0,
+  /** Row major
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next row in memory.
+   */
+  CUBLASLT_ORDER_ROW = 1,
+  /** Column-major ordered tiles of 32 columns.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of next group of 32-columns. E.g. if matrix has 33
+   * columns and 2 rows, ld must be at least (32) * 2 = 64.
+   */
+  CUBLASLT_ORDER_COL32 = 2,
+  /** Column-major ordered tiles of composite tiles with total 32 columns and 8 rows, tile composed of interleaved
+   * inner tiles of 4 columns within 4 even or odd rows in an alternating pattern.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of the first 32 column x 8 row tile for the next
+   * 32-wide group of columns. E.g. if matrix has 33 columns and 1 row, ld must be at least (32 * 8) * 1 = 256.
+   */
+  CUBLASLT_ORDER_COL4_4R2_8C = 3,
+  /** Column-major ordered tiles of composite tiles with total 32 columns ands 32 rows.
+   * Element offset within the tile is calculated as (((row%8)/2*4+row/8)*2+row%2)*32+col.
+   *
+   * Leading dimension is the stride (in elements) to the beginning of the first 32 column x 32 row tile for the next
+   * 32-wide group of columns. E.g. if matrix has 33 columns and 1 row, ld must be at least (32*32)*1 = 1024.
+   */
+  CUBLASLT_ORDER_COL32_2R_4R4 = 4,
+
+} cublasLtOrder_t;
+
+/** Attributes of memory layout */
+typedef enum {
+  /** Data type, see cudaDataType.
+   *
+   * uint32_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_TYPE = 0,
+
+  /** Memory order of the data, see cublasLtOrder_t.
+   *
+   * int32_t, default: CUBLASLT_ORDER_COL
+   */
+  CUBLASLT_MATRIX_LAYOUT_ORDER = 1,
+
+  /** Number of rows.
+   *
+   * Usually only values that can be expressed as int32_t are supported.
+   *
+   * uint64_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_ROWS = 2,
+
+  /** Number of columns.
+   *
+   * Usually only values that can be expressed as int32_t are supported.
+   *
+   * uint64_t
+   */
+  CUBLASLT_MATRIX_LAYOUT_COLS = 3,
+
+  /** Matrix leading dimension.
+   *
+   * For CUBLASLT_ORDER_COL this is stride (in elements) of matrix column, for more details and documentation for
+   * other memory orders see documentation for cublasLtOrder_t values.
+   *
+   * Currently only non-negative values are supported, must be large enough so that matrix memory locations are not
+   * overlapping (e.g. greater or equal to CUBLASLT_MATRIX_LAYOUT_ROWS in case of CUBLASLT_ORDER_COL).
+   *
+   * int64_t;
+   */
+  CUBLASLT_MATRIX_LAYOUT_LD = 4,
+
+  /** Number of matmul operations to perform in the batch.
+   *
+   * See also CUBLASLT_ALGO_CAP_STRIDED_BATCH_SUPPORT
+   *
+   * int32_t, default: 1
+   */
+  CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT = 5,
+
+  /** Stride (in elements) to the next matrix for strided batch operation.
+   *
+   * When matrix type is planar-complex (CUBLASLT_MATRIX_LAYOUT_PLANE_OFFSET != 0), batch stride
+   * is interpreted by cublasLtMatmul() in number of real valued sub-elements. E.g. for data of type CUDA_C_16F,
+   * offset of 1024B is encoded as a stride of value 512 (since each element of the real and imaginary matrices
+   * is a 2B (16bit) floating point type).
+   *
+   * NOTE: A bug in cublasLtMatrixTransform() causes it to interpret the batch stride for a planar-complex matrix
+   * as if it was specified in number of complex elements. Therefore an offset of 1024B must be encoded as stride
+   * value 256 when calling cublasLtMatrixTransform() (each complex element is 4B with real and imaginary values 2B
+   * each). This behavior is expected to be corrected in the next major cuBLAS version.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET = 6,
+
+  /** Stride (in bytes) to the imaginary plane for planar complex layout.
+   *
+   * int64_t, default: 0 - 0 means that layout is regular (real and imaginary parts of complex numbers are interleaved
+   * in memory in each element)
+   */
+  CUBLASLT_MATRIX_LAYOUT_PLANE_OFFSET = 7,
+} cublasLtMatrixLayoutAttribute_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutInit_internal(  //
+    cublasLtMatrixLayout_t matLayout,
+    size_t size,
+    cudaDataType type,
+    uint64_t rows,
+    uint64_t cols,
+    int64_t ld);
+
+/** Initialize matrix layout descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatrixLayoutInit(
+    cublasLtMatrixLayout_t matLayout, cudaDataType type, uint64_t rows, uint64_t cols, int64_t ld) {
+  return cublasLtMatrixLayoutInit_internal(matLayout, sizeof(*matLayout), type, rows, cols, ld);
+}
+
+/** Create new matrix layout descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutCreate(  //
+    cublasLtMatrixLayout_t* matLayout,
+    cudaDataType type,
+    uint64_t rows,
+    uint64_t cols,
+    int64_t ld);
+
+/** Destroy matrix layout descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutDestroy(cublasLtMatrixLayout_t matLayout);
+
+/** Set matrix layout descriptor attribute.
+ *
+ * \param[in]  matLayout    The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutSetAttribute(  //
+    cublasLtMatrixLayout_t matLayout,
+    cublasLtMatrixLayoutAttribute_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matrix layout descriptor attribute.
+ *
+ * \param[in]  matLayout    The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixLayoutGetAttribute(  //
+    cublasLtMatrixLayout_t matLayout,
+    cublasLtMatrixLayoutAttribute_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatmulDesc_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Matmul descriptor attributes to define details of the operation. */
+typedef enum {
+  /** Compute type, see cudaDataType. Defines data type used for multiply and accumulate operations and the
+   * accumulator during matrix multiplication.
+   *
+   * int32_t
+   */
+  CUBLASLT_MATMUL_DESC_COMPUTE_TYPE = 0,
+
+  /** Scale type, see cudaDataType. Defines data type of alpha and beta. Accumulator and value from matrix C are
+   * typically converted to scale type before final scaling. Value is then converted from scale type to type of matrix
+   * D before being stored in memory.
+   *
+   * int32_t, default: same as CUBLASLT_MATMUL_DESC_COMPUTE_TYPE
+   */
+  CUBLASLT_MATMUL_DESC_SCALE_TYPE = 1,
+
+  /** Pointer mode of alpha and beta, see cublasLtPointerMode_t. When CUBLASLT_POINTER_MODE_DEVICE_VECTOR is in use,
+   * alpha/beta vector lenghts must match number of output matrix rows.
+   *
+   * int32_t, default: CUBLASLT_POINTER_MODE_HOST
+   */
+  CUBLASLT_MATMUL_DESC_POINTER_MODE = 2,
+
+  /** Transform of matrix A, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSA = 3,
+
+  /** Transform of matrix B, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSB = 4,
+
+  /** Transform of matrix C, see cublasOperation_t.
+   *
+   * Currently only CUBLAS_OP_N is supported.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATMUL_DESC_TRANSC = 5,
+
+  /** Matrix fill mode, see cublasFillMode_t.
+   *
+   * int32_t, default: CUBLAS_FILL_MODE_FULL
+   */
+  CUBLASLT_MATMUL_DESC_FILL_MODE = 6,
+
+  /** Epilogue function, see cublasLtEpilogue_t.
+   *
+   * uint32_t, default: CUBLASLT_EPILOGUE_DEFAULT
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE = 7,
+
+  /** Bias or bias gradient vector pointer in the device memory.
+   *
+   * Bias case. See CUBLASLT_EPILOGUE_BIAS.
+   * For bias data type see CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE.
+   *
+   * Bias vector length must match matrix D rows count.
+   *
+   * Bias gradient case. See CUBLASLT_EPILOGUE_DRELU_BGRAD and CUBLASLT_EPILOGUE_DGELU_BGRAD.
+   * Bias gradient vector elements are the same type as the output elements
+   * (Ctype) with the exception of IMMA kernels (see above).
+   *
+   * Routines that don't dereference this pointer, like cublasLtMatmulAlgoGetHeuristic()
+   * depend on its value to determine expected pointer alignment.
+   *
+   * Bias case: const void *, default: NULL
+   * Bias gradient case: void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_POINTER = 8,
+
+  /** Batch stride for bias or bias gradient vector.
+   *
+   * Used together with CUBLASLT_MATMUL_DESC_BIAS_POINTER when matrix D's CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT > 1.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_BATCH_STRIDE = 10,
+
+  /** Pointer for epilogue auxiliary buffer.
+   *
+   * - Output vector for ReLu bit-mask in forward pass when CUBLASLT_EPILOGUE_RELU_AUX
+   *   or CUBLASLT_EPILOGUE_RELU_AUX_BIAS epilogue is used.
+   * - Input vector for ReLu bit-mask in backward pass when
+   *   CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is used.
+   *
+   * - Output of GELU input matrix in forward pass when
+   *   CUBLASLT_EPILOGUE_GELU_AUX_BIAS epilogue is used.
+   * - Input of GELU input matrix for backward pass when
+   *   CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue is used.
+   *
+   * For aux data type see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE.
+   *
+   * Routines that don't dereference this pointer, like cublasLtMatmulAlgoGetHeuristic()
+   * depend on its value to determine expected pointer alignment.
+   *
+   * Requires setting CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD attribute.
+   *
+   * Forward pass: void *, default: NULL
+   * Backward pass: const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER = 11,
+
+  /** Leading dimension for epilogue auxiliary buffer.
+   *
+   * - ReLu bit-mask matrix leading dimension in elements (i.e. bits)
+   *   when CUBLASLT_EPILOGUE_RELU_AUX, CUBLASLT_EPILOGUE_RELU_AUX_BIAS or CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is
+   * used. Must be divisible by 128 and be no less than the number of rows in the output matrix.
+   *
+   * - GELU input matrix leading dimension in elements
+   *   when CUBLASLT_EPILOGUE_GELU_AUX_BIAS or CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue used.
+   *   Must be divisible by 8 and be no less than the number of rows in the output matrix.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD = 12,
+
+  /** Batch stride for epilogue auxiliary buffer.
+   *
+   * - ReLu bit-mask matrix batch stride in elements (i.e. bits)
+   *   when CUBLASLT_EPILOGUE_RELU_AUX, CUBLASLT_EPILOGUE_RELU_AUX_BIAS or CUBLASLT_EPILOGUE_DRELU_BGRAD epilogue is
+   * used. Must be divisible by 128.
+   *
+   * - GELU input matrix batch stride in elements
+   *   when CUBLASLT_EPILOGUE_GELU_AUX_BIAS or CUBLASLT_EPILOGUE_DGELU_BGRAD epilogue used.
+   *   Must be divisible by 8.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_BATCH_STRIDE = 13,
+
+  /** Batch stride for alpha vector.
+   *
+   * Used together with CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_HOST when matrix D's
+   * CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT > 1. If CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO is set then
+   * CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE must be set to 0 as this mode doesnt supported batched alpha vector.
+   *
+   * int64_t, default: 0
+   */
+  CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE = 14,
+
+  /** Number of SMs to target for parallel execution. Optimizes heuristics for execution on a different number of SMs
+   *  when user expects a concurrent stream to be using some of the device resources.
+   *
+   *  int32_t, default: 0 - use the number reported by the device.
+   */
+  CUBLASLT_MATMUL_DESC_SM_COUNT_TARGET = 15,
+
+  /** Device pointer to the scale factor value that converts data in matrix A to the compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_A_SCALE_POINTER = 17,
+
+  /** Device pointer to the scale factor value to convert data in matrix B to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_B_SCALE_POINTER = 18,
+
+  /** Device pointer to the scale factor value to convert data in matrix C to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_C_SCALE_POINTER = 19,
+
+  /** Device pointer to the scale factor value to convert data in matrix D to compute data type range.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  const void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_D_SCALE_POINTER = 20,
+
+  /** Device pointer to the memory location that on completion will be set to the maximum of absolute values in the
+   *  output matrix.
+   *
+   *  The computed value has the same type as the compute type.
+   *
+   *  If not specified or set to NULL, the maximum absolute value is not computed. If set for an unsupported matrix
+   *  data, scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_AMAX_D_POINTER = 21,
+
+  /** Type of the data to be stored to the memory pointed to by CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  If unset, the data type defaults to the type of elements of the output matrix with some exceptions, see details
+   * below.
+   *
+   *  ReLu uses a bit-mask.
+   *
+   *  GELU input matrix elements type is the same as the type of elements of
+   *  the output matrix with some exceptions, see details below.
+   *
+   *  For fp8 kernels with output type CUDA_R_8F_E4M3 the aux data type can be CUDA_R_8F_E4M3 or CUDA_R_16F with some
+   *  restrictions.  See https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmulDescAttributes_t for more details.
+   *
+   *  If set for an unsupported matrix data, scale, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  int32_t based on cudaDataType, default: -1
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE = 22,
+
+  /** Device pointer to the scaling factor value to convert results from compute type data range to storage
+   *  data range in the auxiliary matrix that is set via CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  The scaling factor value must have the same type as the compute type.
+   *
+   *  If not specified, or set to NULL, the scaling factor is assumed to be 1. If set for an unsupported matrix data,
+   *  scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_SCALE_POINTER = 23,
+
+  /** Device pointer to the memory location that on completion will be set to the maximum of absolute values in the
+   *  buffer that is set via CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   *
+   *  The computed value has the same type as the compute type.
+   *
+   *  If not specified or set to NULL, the maximum absolute value is not computed. If set for an unsupported matrix
+   *  data, scale, and compute type combination, calling cublasLtMatmul() will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_AMAX_POINTER = 24,
+
+  /** Flag for managing fp8 fast accumulation mode.
+   *  When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results
+   *  will not periodically be promoted to a higher precision.
+   *
+   *  int8_t, default: 0 - fast accumulation mode is disabled.
+   */
+  CUBLASLT_MATMUL_DESC_FAST_ACCUM = 25,
+
+  /** Type of bias or bias gradient vector in the device memory.
+   *
+   * Bias case: see CUBLASLT_EPILOGUE_BIAS.
+   *
+   * Bias vector elements are the same type as the elements of output matrix (Dtype) with the following exceptions:
+   * - IMMA kernels with computeType=CUDA_R_32I and Ctype=CUDA_R_8I where the bias vector elements
+   *   are the same type as alpha, beta (CUBLASLT_MATMUL_DESC_SCALE_TYPE=CUDA_R_32F)
+   * - fp8 kernels with an output type of CUDA_R_32F, CUDA_R_8F_E4M3 or CUDA_R_8F_E5M2, See
+   *   https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmul for details.
+   *
+   * int32_t based on cudaDataType, default: -1
+   */
+  CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE = 26,
+
+  /** EXPERIMENTAL, DEPRECATED: Number of atomic synchronization chunks in the row dimension of the output matrix D.
+   *
+   * int32_t, default 0 (atomic synchronization disabled)
+   */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_ROWS = 27,
+
+  /** EXPERIMENTAL, DEPRECATED: Number of atomic synchronization chunks in the column dimension of the output matrix D.
+   *
+   * int32_t, default 0 (atomic synchronization disabled)
+   */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_COLS = 28,
+
+  /** EXPERIMENTAL: Pointer to a device array of input atomic counters consumed by a matmul.
+   *
+   * int32_t *, default: NULL
+   * */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_IN_COUNTERS_POINTER = 29,
+
+  /** EXPERIMENTAL: Pointer to a device array of output atomic counters produced by a matmul.
+   *
+   * int32_t *, default: NULL
+   * */
+  CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_OUT_COUNTERS_POINTER = 30,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix A is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_A_SCALE_MODE = 31,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix B is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_B_SCALE_MODE = 32,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix C is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_C_SCALE_MODE = 33,
+
+  /** Scaling mode that defines how the matrix scaling factor for matrix D is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_D_SCALE_MODE = 34,
+
+  /** Scaling mode that defines how the matrix scaling factor for the auxiliary matrix is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_SCALE_MODE = 35,
+
+  /** Device pointer to the scale factors that are used to convert data in matrix D to the compute data type range.
+   *
+   *  The scaling factor value type is defined by the scaling mode (see CUBLASLT_MATMUL_DESC_D_OUT_SCALE_MODE)
+   *
+   *  If set for an unsupported matrix data, scale, scale mode, and compute type combination, calling cublasLtMatmul()
+   *  will return CUBLAS_INVALID_VALUE.
+   *
+   *  void *, default: NULL
+   */
+  CUBLASLT_MATMUL_DESC_D_OUT_SCALE_POINTER = 36,
+
+  /** Scaling mode that defines how the output matrix scaling factor for matrix D is interpreted
+   *
+   * int32_t, default: 0 */
+  CUBLASLT_MATMUL_DESC_D_OUT_SCALE_MODE = 37,
+} cublasLtMatmulDescAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescInit_internal(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    size_t size,
+    cublasComputeType_t computeType,
+    cudaDataType_t scaleType);
+
+/** Initialize matmul operation descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was initialized successfully
+ */
+static inline cublasStatus_t cublasLtMatmulDescInit(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasComputeType_t computeType,
+    cudaDataType_t scaleType) {
+  return cublasLtMatmulDescInit_internal(matmulDesc, sizeof(*matmulDesc), computeType, scaleType);
+}
+
+/** Create new matmul operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescCreate(cublasLtMatmulDesc_t* matmulDesc,
+                                                     cublasComputeType_t computeType,
+                                                     cudaDataType_t scaleType);
+
+/** Destroy matmul operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescDestroy(cublasLtMatmulDesc_t matmulDesc);
+
+/** Set matmul operation descriptor attribute.
+ *
+ * \param[in]  matmulDesc   The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescSetAttribute(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasLtMatmulDescAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matmul operation descriptor attribute.
+ *
+ * \param[in]  matmulDesc   The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulDescGetAttribute(  //
+    cublasLtMatmulDesc_t matmulDesc,
+    cublasLtMatmulDescAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Helper functions for cublasLtMatrixTransformDesc_t */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Matrix transform descriptor attributes to define details of the operation.
+ */
+typedef enum {
+  /** Scale type, see cudaDataType. Inputs are converted to scale type for scaling and summation and results are then
+   * converted to output type to store in memory.
+   *
+   * int32_t
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_SCALE_TYPE,
+
+  /** Pointer mode of alpha and beta, see cublasLtPointerMode_t.
+   *
+   * int32_t, default: CUBLASLT_POINTER_MODE_HOST
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_POINTER_MODE,
+
+  /** Transform of matrix A, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_TRANSA,
+
+  /** Transform of matrix B, see cublasOperation_t.
+   *
+   * int32_t, default: CUBLAS_OP_N
+   */
+  CUBLASLT_MATRIX_TRANSFORM_DESC_TRANSB,
+} cublasLtMatrixTransformDescAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescInit_internal(cublasLtMatrixTransformDesc_t transformDesc,
+                                                                     size_t size,
+                                                                     cudaDataType scaleType);
+
+/** Initialize matrix transform operation descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatrixTransformDescInit(cublasLtMatrixTransformDesc_t transformDesc,
+                                                             cudaDataType scaleType) {
+  return cublasLtMatrixTransformDescInit_internal(transformDesc, sizeof(*transformDesc), scaleType);
+}
+
+/** Create new matrix transform operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescCreate(cublasLtMatrixTransformDesc_t* transformDesc,
+                                                              cudaDataType scaleType);
+
+/** Destroy matrix transform operation descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescDestroy(cublasLtMatrixTransformDesc_t transformDesc);
+
+/** Set matrix transform operation descriptor attribute.
+ *
+ * \param[in]  transformDesc  The descriptor
+ * \param[in]  attr           The attribute
+ * \param[in]  buf            memory address containing the new value
+ * \param[in]  sizeInBytes    size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescSetAttribute(  //
+    cublasLtMatrixTransformDesc_t transformDesc,
+    cublasLtMatrixTransformDescAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matrix transform operation descriptor attribute.
+ *
+ * \param[in]  transformDesc  The descriptor
+ * \param[in]  attr           The attribute
+ * \param[out] buf            memory address containing the new value
+ * \param[in]  sizeInBytes    size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten    only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number
+ * of bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatrixTransformDescGetAttribute(  //
+    cublasLtMatrixTransformDesc_t transformDesc,
+    cublasLtMatrixTransformDescAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/** Reduction scheme for portions of the dot-product calculated in parallel (a. k. a. "split - K").
+ */
+typedef enum {
+  /** No reduction scheme, dot-product shall be performed in one sequence.
+   */
+  CUBLASLT_REDUCTION_SCHEME_NONE = 0,
+
+  /** Reduction is performed "in place" - using the output buffer (and output data type) and counters (in workspace) to
+   * guarantee the sequentiality.
+   */
+  CUBLASLT_REDUCTION_SCHEME_INPLACE = 1,
+
+  /** Intermediate results are stored in compute type in the workspace and reduced in a separate step.
+   */
+  CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE = 2,
+
+  /** Intermediate results are stored in output type in the workspace and reduced in a separate step.
+   */
+  CUBLASLT_REDUCTION_SCHEME_OUTPUT_TYPE = 4,
+
+  CUBLASLT_REDUCTION_SCHEME_MASK = 0x7,
+} cublasLtReductionScheme_t;
+
+/** Postprocessing options for the epilogue
+ */
+typedef enum {
+  /** No special postprocessing, just scale and quantize results if necessary.
+   */
+  CUBLASLT_EPILOGUE_DEFAULT = 1,
+
+  /** ReLu, apply ReLu point-wise transform to the results (x:=max(x, 0)).
+   */
+  CUBLASLT_EPILOGUE_RELU = 2,
+
+  /** ReLu, apply ReLu point-wise transform to the results (x:=max(x, 0)).
+   *
+   * This epilogue mode produces an extra output, a ReLu bit-mask matrix,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_RELU_AUX = (CUBLASLT_EPILOGUE_RELU | 128),
+
+  /** Bias, apply (broadcasted) Bias from bias vector. Bias vector length must match matrix D rows, it must be packed
+   * (stride between vector elements is 1). Bias vector is broadcasted to all columns and added before applying final
+   * postprocessing.
+   */
+  CUBLASLT_EPILOGUE_BIAS = 4,
+
+  /** ReLu and Bias, apply Bias and then ReLu transform
+   */
+  CUBLASLT_EPILOGUE_RELU_BIAS = (CUBLASLT_EPILOGUE_RELU | CUBLASLT_EPILOGUE_BIAS),
+
+  /** ReLu and Bias, apply Bias and then ReLu transform
+   *
+   * This epilogue mode produces an extra output, a ReLu bit-mask matrix,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_RELU_AUX_BIAS = (CUBLASLT_EPILOGUE_RELU_AUX | CUBLASLT_EPILOGUE_BIAS),
+
+  /* ReLu gradient. Apply ReLu gradient to matmul output. Store ReLu gradient in the output matrix.
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DRELU = 8 | 128,
+
+  /* ReLu and Bias gradients. Apply independently ReLu and Bias gradient to
+   * matmul output. Store ReLu gradient in the output matrix, and Bias gradient
+   * in the auxiliary output (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DRELU_BGRAD = CUBLASLT_EPILOGUE_DRELU | 16,
+
+  /** GELU, apply GELU point-wise transform to the results (x:=GELU(x)).
+   */
+  CUBLASLT_EPILOGUE_GELU = 32,
+
+  /** GELU, apply GELU point-wise transform to the results (x:=GELU(x)).
+   *
+   * This epilogue mode outputs GELU input as a separate matrix (useful for training).
+   * See CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_GELU_AUX = (CUBLASLT_EPILOGUE_GELU | 128),
+
+  /** GELU and Bias, apply Bias and then GELU transform
+   */
+  CUBLASLT_EPILOGUE_GELU_BIAS = (CUBLASLT_EPILOGUE_GELU | CUBLASLT_EPILOGUE_BIAS),
+
+  /** GELU and Bias, apply Bias and then GELU transform
+   *
+   * This epilogue mode outputs GELU input as a separate matrix (useful for training).
+   * See CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_GELU_AUX_BIAS = (CUBLASLT_EPILOGUE_GELU_AUX | CUBLASLT_EPILOGUE_BIAS),
+
+  /* GELU gradient. Apply GELU gradient to matmul output. Store GELU gradient in the output matrix.
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DGELU = 64 | 128,
+
+  /* GELU and Bias gradients. Apply independently GELU and Bias gradient to
+   * matmul output. Store GELU gradient in the output matrix, and Bias gradient
+   * in the auxiliary output (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   *
+   * This epilogue mode requires an extra input,
+   * see CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER.
+   */
+  CUBLASLT_EPILOGUE_DGELU_BGRAD = CUBLASLT_EPILOGUE_DGELU | 16,
+
+  /** Bias gradient based on the input matrix A.
+   *
+   * The bias size corresponds to the number of rows of the matrix D.
+   * The reduction happens over the GEMM's "k" dimension.
+   *
+   * Stores Bias gradient in the auxiliary output
+   * (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   */
+  CUBLASLT_EPILOGUE_BGRADA = 256,
+
+  /** Bias gradient based on the input matrix B.
+   *
+   * The bias size corresponds to the number of columns of the matrix D.
+   * The reduction happens over the GEMM's "k" dimension.
+   *
+   * Stores Bias gradient in the auxiliary output
+   * (see CUBLASLT_MATMUL_DESC_BIAS_POINTER).
+   */
+  CUBLASLT_EPILOGUE_BGRADB = 512,
+} cublasLtEpilogue_t;
+
+/** Matmul heuristic search mode
+ */
+typedef enum {
+  /** ask heuristics for best algo for given usecase
+   */
+  CUBLASLT_SEARCH_BEST_FIT = 0,
+  /** only try to find best config for preconfigured algo id
+   */
+  CUBLASLT_SEARCH_LIMITED_BY_ALGO_ID = 1,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_02 = 2,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_03 = 3,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_04 = 4,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_05 = 5,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_06 = 6,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_07 = 7,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_08 = 8,
+  /** reserved for future use
+   */
+  CUBLASLT_SEARCH_RESERVED_09 = 9,
+} cublasLtMatmulSearch_t;
+
+/** Algo search preference to fine tune the heuristic function. */
+typedef enum {
+  /** Search mode, see cublasLtMatmulSearch_t.
+   *
+   * uint32_t, default: CUBLASLT_SEARCH_BEST_FIT
+   */
+  CUBLASLT_MATMUL_PREF_SEARCH_MODE = 0,
+
+  /** Maximum allowed workspace size in bytes.
+   *
+   * uint64_t, default: 0 - no workspace allowed
+   */
+  CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES = 1,
+
+  /** Reduction scheme mask, see cublasLtReductionScheme_t. Filters heuristic result to only include algo configs that
+   * use one of the required modes.
+   *
+   * E.g. mask value of 0x03 will allow only INPLACE and COMPUTE_TYPE reduction schemes.
+   *
+   * uint32_t, default: CUBLASLT_REDUCTION_SCHEME_MASK (allows all reduction schemes)
+   */
+  CUBLASLT_MATMUL_PREF_REDUCTION_SCHEME_MASK = 3,
+
+  /** Minimum buffer alignment for matrix A (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix A that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_A_BYTES = 5,
+
+  /** Minimum buffer alignment for matrix B (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix B that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_B_BYTES = 6,
+
+  /** Minimum buffer alignment for matrix C (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix C that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_C_BYTES = 7,
+
+  /** Minimum buffer alignment for matrix D (in bytes).
+   *
+   * Selecting a smaller value will exclude algorithms that can not work with matrix D that is not as strictly aligned
+   * as they need.
+   *
+   * uint32_t, default: 256
+   */
+  CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_D_BYTES = 8,
+
+  /** Maximum wave count.
+   *
+   * See cublasLtMatmulHeuristicResult_t::wavesCount.
+   *
+   * Selecting a non-zero value will exclude algorithms that report device utilization higher than specified.
+   *
+   * float, default: 0.0f
+   */
+  CUBLASLT_MATMUL_PREF_MAX_WAVES_COUNT = 9,
+
+  /** Numerical implementation details mask, see cublasLtNumericalImplFlags_t. Filters heuristic result to only include
+   * algorithms that use the allowed implementations.
+   *
+   * uint64_t, default: uint64_t(-1) (allow everything)
+   */
+  CUBLASLT_MATMUL_PREF_IMPL_MASK = 12,
+} cublasLtMatmulPreferenceAttributes_t;
+
+/** Internal. Do not use directly.
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceInit_internal(cublasLtMatmulPreference_t pref, size_t size);
+
+/** Initialize matmul heuristic search preference descriptor in pre-allocated space.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if size of the pre-allocated space is insufficient
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+static inline cublasStatus_t cublasLtMatmulPreferenceInit(cublasLtMatmulPreference_t pref) {
+  return cublasLtMatmulPreferenceInit_internal(pref, sizeof(*pref));
+}
+
+/** Create new matmul heuristic search preference descriptor.
+ *
+ * \retval     CUBLAS_STATUS_ALLOC_FAILED  if memory could not be allocated
+ * \retval     CUBLAS_STATUS_SUCCESS       if desciptor was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceCreate(cublasLtMatmulPreference_t* pref);
+
+/** Destroy matmul heuristic search preference descriptor.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS  if operation was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceDestroy(cublasLtMatmulPreference_t pref);
+
+/** Set matmul heuristic search preference descriptor attribute.
+ *
+ * \param[in]  pref         The descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceSetAttribute(  //
+    cublasLtMatmulPreference_t pref,
+    cublasLtMatmulPreferenceAttributes_t attr,
+    const void* buf,
+    size_t sizeInBytes);
+
+/** Get matmul heuristic search preference descriptor attribute.
+ *
+ * \param[in]  pref         The descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulPreferenceGetAttribute(  //
+    cublasLtMatmulPreference_t pref,
+    cublasLtMatmulPreferenceAttributes_t attr,
+    void* buf,
+    size_t sizeInBytes,
+    size_t* sizeWritten);
+
+/** Results structure used by cublasLtMatmulAlgoGetHeuristic
+ *
+ * Holds returned configured algo descriptor and its runtime properties.
+ */
+typedef struct {
+  /** Matmul algorithm descriptor.
+   *
+   * Must be initialized with cublasLtMatmulAlgoInit() if preferences' CUBLASLT_MATMUL_PERF_SEARCH_MODE is set to
+   * CUBLASLT_SEARCH_LIMITED_BY_ALGO_ID
+   */
+  cublasLtMatmulAlgo_t algo;
+
+  /** Actual size of workspace memory required.
+   */
+  size_t workspaceSize;
+
+  /** Result status, other fields are only valid if after call to cublasLtMatmulAlgoGetHeuristic() this member is set to
+   * CUBLAS_STATUS_SUCCESS.
+   */
+  cublasStatus_t state;
+
+  /** Waves count - a device utilization metric.
+   *
+   * wavesCount value of 1.0f suggests that when kernel is launched it will fully occupy the GPU.
+   */
+  float wavesCount;
+
+  int reserved[4];
+} cublasLtMatmulHeuristicResult_t;
+
+/** Query cublasLt heuristic for algorithm appropriate for given use case.
+ *
+ * \param[in]      lightHandle            Pointer to the allocated cuBLASLt handle for the cuBLASLt
+ *                                        context. See cublasLtHandle_t.
+ * \param[in]      operationDesc          Handle to the matrix multiplication descriptor.
+ * \param[in]      Adesc                  Handle to the layout descriptors for matrix A.
+ * \param[in]      Bdesc                  Handle to the layout descriptors for matrix B.
+ * \param[in]      Cdesc                  Handle to the layout descriptors for matrix C.
+ * \param[in]      Ddesc                  Handle to the layout descriptors for matrix D.
+ * \param[in]      preference             Pointer to the structure holding the heuristic search
+ *                                        preferences descriptor. See cublasLtMatrixLayout_t.
+ * \param[in]      requestedAlgoCount     Size of heuristicResultsArray (in elements) and requested
+ *                                        maximum number of algorithms to return.
+ * \param[in, out] heuristicResultsArray  Output algorithms and associated runtime characteristics,
+ *                                        ordered in increasing estimated compute time.
+ * \param[out]     returnAlgoCount        The number of heuristicResultsArray elements written.
+ *
+ * \retval  CUBLAS_STATUS_INVALID_VALUE   if requestedAlgoCount is less or equal to zero
+ * \retval  CUBLAS_STATUS_NOT_SUPPORTED   if no heuristic function available for current configuration
+ * \retval  CUBLAS_STATUS_SUCCESS         if query was successful, inspect
+ *                                        heuristicResultsArray[0 to (returnAlgoCount - 1)].state
+ *                                        for detail status of results
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoGetHeuristic(cublasLtHandle_t lightHandle,
+                                                           cublasLtMatmulDesc_t operationDesc,
+                                                           cublasLtMatrixLayout_t Adesc,
+                                                           cublasLtMatrixLayout_t Bdesc,
+                                                           cublasLtMatrixLayout_t Cdesc,
+                                                           cublasLtMatrixLayout_t Ddesc,
+                                                           cublasLtMatmulPreference_t preference,
+                                                           int requestedAlgoCount,
+                                                           cublasLtMatmulHeuristicResult_t heuristicResultsArray[],
+                                                           int* returnAlgoCount);
+
+/* ---------------------------------------------------------------------------------------*/
+/* Lower level API to be able to implement own Heuristic and Find routines                */
+/* ---------------------------------------------------------------------------------------*/
+
+/** Routine to get all algo IDs that can potentially run
+ *
+ * \param[in]  int              requestedAlgoCount requested number of algos (must be less or equal to size of algoIdsA
+ * (in elements)) \param[out] algoIdsA         array to write algoIds to \param[out] returnAlgoCount  number of algoIds
+ * actually written
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if requestedAlgoCount is less or equal to zero
+ * \retval     CUBLAS_STATUS_SUCCESS        if query was successful, inspect returnAlgoCount to get actual number of IDs
+ *                                          available
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoGetIds(cublasLtHandle_t lightHandle,
+                                                     cublasComputeType_t computeType,
+                                                     cudaDataType_t scaleType,
+                                                     cudaDataType_t Atype,
+                                                     cudaDataType_t Btype,
+                                                     cudaDataType_t Ctype,
+                                                     cudaDataType_t Dtype,
+                                                     int requestedAlgoCount,
+                                                     int algoIdsArray[],
+                                                     int* returnAlgoCount);
+
+/** Initialize algo structure
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if algo is NULL or algoId is outside of recognized range
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED  if algoId is not supported for given combination of data types
+ * \retval     CUBLAS_STATUS_SUCCESS        if the structure was successfully initialized
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoInit(cublasLtHandle_t lightHandle,
+                                                   cublasComputeType_t computeType,
+                                                   cudaDataType_t scaleType,
+                                                   cudaDataType_t Atype,
+                                                   cudaDataType_t Btype,
+                                                   cudaDataType_t Ctype,
+                                                   cudaDataType_t Dtype,
+                                                   int algoId,
+                                                   cublasLtMatmulAlgo_t* algo);
+
+/** Check configured algo descriptor for correctness and support on current device.
+ *
+ * Result includes required workspace size and calculated wave count.
+ *
+ * CUBLAS_STATUS_SUCCESS doesn't fully guarantee algo will run (will fail if e.g. buffers are not correctly aligned);
+ * but if cublasLtMatmulAlgoCheck fails, the algo will not run.
+ *
+ * \param[in]  algo    algo configuration to check
+ * \param[out] result  result structure to report algo runtime characteristics; algo field is never updated
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if matrix layout descriptors or operation descriptor don't match algo
+ *                                          descriptor
+ * \retval     CUBLAS_STATUS_NOT_SUPPORTED  if algo configuration or data type combination is not currently supported on
+ *                                          given device
+ * \retval     CUBLAS_STATUS_ARCH_MISMATCH  if algo configuration cannot be run using the selected device
+ * \retval     CUBLAS_STATUS_SUCCESS        if check was successful
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoCheck(  //
+    cublasLtHandle_t lightHandle,
+    cublasLtMatmulDesc_t operationDesc,
+    cublasLtMatrixLayout_t Adesc,
+    cublasLtMatrixLayout_t Bdesc,
+    cublasLtMatrixLayout_t Cdesc,
+    cublasLtMatrixLayout_t Ddesc,
+    const cublasLtMatmulAlgo_t* algo,  ///< may point to result->algo
+    cublasLtMatmulHeuristicResult_t* result);
+
+/** Capabilities Attributes that can be retrieved from an initialized Algo structure
+ */
+typedef enum {
+  /** support for split K, see CUBLASLT_ALGO_CONFIG_SPLITK_NUM
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_SPLITK_SUPPORT = 0,
+
+  /** reduction scheme mask, see cublasLtReductionScheme_t; shows supported reduction schemes, if reduction scheme is
+   * not masked out it is supported.
+   *
+   * e.g. int isReductionSchemeComputeTypeSupported ? (reductionSchemeMask & CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE) ==
+   * CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE ? 1 : 0;
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK = 1,
+
+  /** support for cta swizzling, see CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING
+   *
+   * uint32_t, 0 means no support, 1 means supported value of 1, other values are reserved
+   */
+  CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT = 2,
+
+  /** support strided batch
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_STRIDED_BATCH_SUPPORT = 3,
+
+  /** support results out of place (D != C in D = alpha.A.B + beta.C)
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_OUT_OF_PLACE_RESULT_SUPPORT = 4,
+
+  /** syrk/herk support (on top of regular gemm)
+   *
+   * int32_t, 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_UPLO_SUPPORT = 5,
+
+  /** tile ids possible to use, see cublasLtMatmulTile_t; if no tile ids are supported use
+   * CUBLASLT_MATMUL_TILE_UNDEFINED
+   *
+   * use cublasLtMatmulAlgoCapGetAttribute() with sizeInBytes=0 to query actual count
+   *
+   * array of uint32_t
+   */
+  CUBLASLT_ALGO_CAP_TILE_IDS = 6,
+
+  /** custom option range is from 0 to CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX (inclusive), see
+   * CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION
+   *
+   * int32_t
+   */
+  CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX = 7,
+
+  /** whether algorithm supports custom (not COL or ROW memory order), see cublasLtOrder_t
+   *
+   * int32_t 0 means only COL and ROW memory order is allowed, non-zero means that algo might have different
+   * requirements;
+   */
+  CUBLASLT_ALGO_CAP_CUSTOM_MEMORY_ORDER = 10,
+
+  /** bitmask enumerating pointer modes algorithm supports
+   *
+   * uint32_t, see cublasLtPointerModeMask_t
+   */
+  CUBLASLT_ALGO_CAP_POINTER_MODE_MASK = 11,
+
+  /** bitmask enumerating kinds of postprocessing algorithm supports in the epilogue
+   *
+   * uint32_t, see cublasLtEpilogue_t
+   */
+  CUBLASLT_ALGO_CAP_EPILOGUE_MASK = 12,
+
+  /** stages ids possible to use, see cublasLtMatmulStages_t; if no stages ids are supported use
+   * CUBLASLT_MATMUL_STAGES_UNDEFINED
+   *
+   * use cublasLtMatmulAlgoCapGetAttribute() with sizeInBytes=0 to query actual count
+   *
+   * array of uint32_t
+   */
+  CUBLASLT_ALGO_CAP_STAGES_IDS = 13,
+
+  /** support for nagative ld for all of the matrices
+   *
+   * int32_t 0 means no support, supported otherwise
+   */
+  CUBLASLT_ALGO_CAP_LD_NEGATIVE = 14,
+
+  /** details about algorithm's implementation that affect it's numerical behavior
+   *
+   * uint64_t, see cublasLtNumericalImplFlags_t
+   */
+  CUBLASLT_ALGO_CAP_NUMERICAL_IMPL_FLAGS = 15,
+
+  /** minimum alignment required for A matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_A_BYTES = 16,
+
+  /** minimum alignment required for B matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_B_BYTES = 17,
+
+  /** minimum alignment required for C matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_C_BYTES = 18,
+
+  /** minimum alignment required for D matrix in bytes
+   *  (required for buffer pointer, leading dimension, and possibly other strides defined for matrix memory order)
+   *
+   * uint32_t
+   */
+  CUBLASLT_ALGO_CAP_MIN_ALIGNMENT_D_BYTES = 19,
+
+  /** EXPERIMENTAL: support for synchronization via atomic counters
+   *
+   * int32_t
+   */
+  CUBLASLT_ALGO_CAP_ATOMIC_SYNC = 20,
+} cublasLtMatmulAlgoCapAttributes_t;
+
+/** Get algo capability attribute.
+ *
+ * E.g. to get list of supported Tile IDs:
+ *      cublasLtMatmulTile_t tiles[CUBLASLT_MATMUL_TILE_END];
+ *      size_t num_tiles, size_written;
+ *      if (cublasLtMatmulAlgoCapGetAttribute(algo, CUBLASLT_ALGO_CAP_TILE_IDS, tiles, sizeof(tiles), size_written) ==
+ * CUBLAS_STATUS_SUCCESS) { num_tiles = size_written / sizeof(tiles[0]);
+ *      }
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoCapGetAttribute(const cublasLtMatmulAlgo_t* algo,
+                                                              cublasLtMatmulAlgoCapAttributes_t attr,
+                                                              void* buf,
+                                                              size_t sizeInBytes,
+                                                              size_t* sizeWritten);
+
+/** Algo Configuration Attributes that can be set according to the Algo capabilities
+ */
+typedef enum {
+  /** algorithm index, see cublasLtMatmulAlgoGetIds()
+   *
+   * readonly, set by cublasLtMatmulAlgoInit()
+   * int32_t
+   */
+  CUBLASLT_ALGO_CONFIG_ID = 0,
+  /** tile id, see cublasLtMatmulTile_t
+   *
+   * uint32_t, default: CUBLASLT_MATMUL_TILE_UNDEFINED
+   */
+  CUBLASLT_ALGO_CONFIG_TILE_ID = 1,
+  /** Number of K splits. If the number of K splits is greater than one, SPLITK_NUM parts
+   * of matrix multiplication will be computed in parallel. The results will be accumulated
+   * according to CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME
+   *
+   * int32_t, default: 1
+   */
+  CUBLASLT_ALGO_CONFIG_SPLITK_NUM = 2,
+  /** reduction scheme, see cublasLtReductionScheme_t
+   *
+   * uint32_t, default: CUBLASLT_REDUCTION_SCHEME_NONE
+   */
+  CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME = 3,
+  /** cta swizzling, change mapping from CUDA grid coordinates to parts of the matrices
+   *
+   * possible values: 0, 1, other values reserved
+   *
+   * uint32_t, default: 0
+   */
+  CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING = 4,
+  /** custom option, each algorithm can support some custom options that don't fit description of the other config
+   * attributes, see CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX to get accepted range for any specific case
+   *
+   * uint32_t, default: 0
+   */
+  CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION = 5,
+  /** stages id, see cublasLtMatmulStages_t
+   *
+   * uint32_t, default: CUBLASLT_MATMUL_STAGES_UNDEFINED
+   */
+  CUBLASLT_ALGO_CONFIG_STAGES_ID = 6,
+  /** inner shape id, see cublasLtMatmulInnerShape_t
+   *
+   * uint16_t, default: 0 (CUBLASLT_MATMUL_INNER_SHAPE_UNDEFINED)
+   */
+  CUBLASLT_ALGO_CONFIG_INNER_SHAPE_ID = 7,
+  /** Thread Block Cluster shape id, see cublasLtClusterShape_t. Defines cluster size to use.
+   *
+   * uint16_t, default: 0 (CUBLASLT_CLUSTER_SHAPE_AUTO)
+   */
+  CUBLASLT_ALGO_CONFIG_CLUSTER_SHAPE_ID = 8,
+} cublasLtMatmulAlgoConfigAttributes_t;
+
+/** Set algo configuration attribute.
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[in]  buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoConfigSetAttribute(cublasLtMatmulAlgo_t* algo,
+                                                                 cublasLtMatmulAlgoConfigAttributes_t attr,
+                                                                 const void* buf,
+                                                                 size_t sizeInBytes);
+
+/** Get algo configuration attribute.
+ *
+ * \param[in]  algo         The algo descriptor
+ * \param[in]  attr         The attribute
+ * \param[out] buf          memory address containing the new value
+ * \param[in]  sizeInBytes  size of buf buffer for verification (in bytes)
+ * \param[out] sizeWritten  only valid when return value is CUBLAS_STATUS_SUCCESS. If sizeInBytes is non-zero: number of
+ *                          bytes actually written, if sizeInBytes is 0: number of bytes needed to write full contents
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if sizeInBytes is 0 and sizeWritten is NULL, or if  sizeInBytes is non-zero
+ *                                          and buf is NULL or sizeInBytes doesn't match size of internal storage for
+ *                                          selected attribute
+ * \retval     CUBLAS_STATUS_SUCCESS        if attribute's value was successfully written to user memory
+ */
+cublasStatus_t CUBLASWINAPI cublasLtMatmulAlgoConfigGetAttribute(const cublasLtMatmulAlgo_t* algo,
+                                                                 cublasLtMatmulAlgoConfigAttributes_t attr,
+                                                                 void* buf,
+                                                                 size_t sizeInBytes,
+                                                                 size_t* sizeWritten);
+
+/** Experimental: Logger callback type.
+ */
+typedef void (*cublasLtLoggerCallback_t)(int logLevel, const char* functionName, const char* message);
+
+/** Experimental: Logger callback setter.
+ *
+ * \param[in]  callback                     a user defined callback function to be called by the logger
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if callback was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetCallback(cublasLtLoggerCallback_t callback);
+
+/** Experimental: Log file setter.
+ *
+ * \param[in]  file                         an open file with write permissions
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log file was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetFile(FILE* file);
+
+/** Experimental: Open log file.
+ *
+ * \param[in]  logFile                      log file path. if the log file does not exist, it will be created
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log file was created successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerOpenFile(const char* logFile);
+
+/** Experimental: Log level setter.
+ *
+ * \param[in]  level                        log level, should be one of the following:
+ *                                          0. Off
+ *                                          1. Errors
+ *                                          2. Performance Trace
+ *                                          3. Performance Hints
+ *                                          4. Heuristics Trace
+ *                                          5. API Trace
+ *
+ * \retval     CUBLAS_STATUS_INVALID_VALUE  if log level is not one of the above levels
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log level was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetLevel(int level);
+
+/** Experimental: Log mask setter.
+ *
+ * \param[in]  mask                         log mask, should be a combination of the following masks:
+ *                                          0.  Off
+ *                                          1.  Errors
+ *                                          2.  Performance Trace
+ *                                          4.  Performance Hints
+ *                                          8.  Heuristics Trace
+ *                                          16. API Trace
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if log mask was set successfully
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerSetMask(int mask);
+
+/** Experimental: Disable logging for the entire session.
+ *
+ * \retval     CUBLAS_STATUS_SUCCESS        if disabled logging
+ */
+cublasStatus_t CUBLASWINAPI cublasLtLoggerForceDisable();
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
diff --git a/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasXt.h b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasXt.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe0e6f99b952514874c45208e751f5330e71570c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/nvidia/cublas/include/cublasXt.h
@@ -0,0 +1,693 @@
+/*
+ * Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cublasXt : Host API, Out of Core and Multi-GPU BLAS Library
+
+*/
+
+#if !defined(CUBLAS_XT_H_)
+#define CUBLAS_XT_H_
+
+#include "driver_types.h"
+#include "cuComplex.h" /* import complex data type */
+
+#include "cublas_v2.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif /* __cplusplus */
+
+struct cublasXtContext;
+typedef struct cublasXtContext* cublasXtHandle_t;
+
+cublasStatus_t CUBLASWINAPI cublasXtCreate(cublasXtHandle_t* handle);
+cublasStatus_t CUBLASWINAPI cublasXtDestroy(cublasXtHandle_t handle);
+cublasStatus_t CUBLASWINAPI cublasXtGetNumBoards(int nbDevices, int deviceId[], int* nbBoards);
+cublasStatus_t CUBLASWINAPI cublasXtMaxBoards(int* nbGpuBoards);
+/* This routine selects the Gpus that the user want to use for CUBLAS-XT */
+cublasStatus_t CUBLASWINAPI cublasXtDeviceSelect(cublasXtHandle_t handle, int nbDevices, int deviceId[]);
+
+/* This routine allows to change the dimension of the tiles ( blockDim x blockDim ) */
+cublasStatus_t CUBLASWINAPI cublasXtSetBlockDim(cublasXtHandle_t handle, int blockDim);
+cublasStatus_t CUBLASWINAPI cublasXtGetBlockDim(cublasXtHandle_t handle, int* blockDim);
+
+typedef enum { CUBLASXT_PINNING_DISABLED = 0, CUBLASXT_PINNING_ENABLED = 1 } cublasXtPinnedMemMode_t;
+/* This routine allows to CUBLAS-XT to pin the Host memory if it find out that some of the matrix passed
+   are not pinned : Pinning/Unpinning the Host memory is still a costly operation
+   It is better if the user controls the memory on its own (by pinning/unpinning oly when necessary)
+*/
+cublasStatus_t CUBLASWINAPI cublasXtGetPinningMemMode(cublasXtHandle_t handle, cublasXtPinnedMemMode_t* mode);
+cublasStatus_t CUBLASWINAPI cublasXtSetPinningMemMode(cublasXtHandle_t handle, cublasXtPinnedMemMode_t mode);
+
+/* This routines is to provide a CPU Blas routines, used for too small sizes or hybrid computation */
+typedef enum {
+  CUBLASXT_FLOAT = 0,
+  CUBLASXT_DOUBLE = 1,
+  CUBLASXT_COMPLEX = 2,
+  CUBLASXT_DOUBLECOMPLEX = 3,
+} cublasXtOpType_t;
+
+typedef enum {
+  CUBLASXT_GEMM = 0,
+  CUBLASXT_SYRK = 1,
+  CUBLASXT_HERK = 2,
+  CUBLASXT_SYMM = 3,
+  CUBLASXT_HEMM = 4,
+  CUBLASXT_TRSM = 5,
+  CUBLASXT_SYR2K = 6,
+  CUBLASXT_HER2K = 7,
+
+  CUBLASXT_SPMM = 8,
+  CUBLASXT_SYRKX = 9,
+  CUBLASXT_HERKX = 10,
+  CUBLASXT_TRMM = 11,
+  CUBLASXT_ROUTINE_MAX = 12,
+} cublasXtBlasOp_t;
+
+/* Currently only 32-bit integer BLAS routines are supported */
+cublasStatus_t CUBLASWINAPI cublasXtSetCpuRoutine(cublasXtHandle_t handle,
+                                                  cublasXtBlasOp_t blasOp,
+                                                  cublasXtOpType_t type,
+                                                  void* blasFunctor);
+
+/* Specified the percentage of work that should done by the CPU, default is 0 (no work) */
+cublasStatus_t CUBLASWINAPI cublasXtSetCpuRatio(cublasXtHandle_t handle,
+                                                cublasXtBlasOp_t blasOp,
+                                                cublasXtOpType_t type,
+                                                float ratio);
+
+/* GEMM */
+cublasStatus_t CUBLASWINAPI cublasXtSgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZgemm(cublasXtHandle_t handle,
+                                          cublasOperation_t transa,
+                                          cublasOperation_t transb,
+                                          size_t m,
+                                          size_t n,
+                                          size_t k,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* ------------------------------------------------------- */
+/* SYRK */
+cublasStatus_t CUBLASWINAPI cublasXtSsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyrk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HERK */
+cublasStatus_t CUBLASWINAPI cublasXtCherk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const float* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const float* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZherk(cublasXtHandle_t handle,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          size_t n,
+                                          size_t k,
+                                          const double* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const double* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* SYR2K */
+cublasStatus_t CUBLASWINAPI cublasXtSsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const float* alpha,
+                                           const float* A,
+                                           size_t lda,
+                                           const float* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           float* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const double* alpha,
+                                           const double* A,
+                                           size_t lda,
+                                           const double* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           double* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const cuComplex* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyr2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const cuDoubleComplex* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HERKX : variant extension of HERK */
+cublasStatus_t CUBLASWINAPI cublasXtCherkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZherkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* TRSM */
+cublasStatus_t CUBLASWINAPI cublasXtStrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          float* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtDtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          double* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtCtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          cuComplex* B,
+                                          size_t ldb);
+
+cublasStatus_t CUBLASWINAPI cublasXtZtrsm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          cuDoubleComplex* B,
+                                          size_t ldb);
+/* -------------------------------------------------------------------- */
+/* SYMM : Symmetric Multiply Matrix*/
+cublasStatus_t CUBLASWINAPI cublasXtSsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsymm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HEMM : Hermitian Matrix Multiply */
+cublasStatus_t CUBLASWINAPI cublasXtChemm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZhemm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* SYRKX : variant extension of SYRK  */
+cublasStatus_t CUBLASWINAPI cublasXtSsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const float* alpha,
+                                           const float* A,
+                                           size_t lda,
+                                           const float* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           float* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const double* alpha,
+                                           const double* A,
+                                           size_t lda,
+                                           const double* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           double* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const cuComplex* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZsyrkx(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const cuDoubleComplex* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+/* -------------------------------------------------------------------- */
+/* HER2K : variant extension of HERK  */
+cublasStatus_t CUBLASWINAPI cublasXtCher2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuComplex* alpha,
+                                           const cuComplex* A,
+                                           size_t lda,
+                                           const cuComplex* B,
+                                           size_t ldb,
+                                           const float* beta,
+                                           cuComplex* C,
+                                           size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZher2k(cublasXtHandle_t handle,
+                                           cublasFillMode_t uplo,
+                                           cublasOperation_t trans,
+                                           size_t n,
+                                           size_t k,
+                                           const cuDoubleComplex* alpha,
+                                           const cuDoubleComplex* A,
+                                           size_t lda,
+                                           const cuDoubleComplex* B,
+                                           size_t ldb,
+                                           const double* beta,
+                                           cuDoubleComplex* C,
+                                           size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* SPMM : Symmetric Packed Multiply Matrix*/
+cublasStatus_t CUBLASWINAPI cublasXtSspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* AP,
+                                          const float* B,
+                                          size_t ldb,
+                                          const float* beta,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* AP,
+                                          const double* B,
+                                          size_t ldb,
+                                          const double* beta,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* AP,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          const cuComplex* beta,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZspmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* AP,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          const cuDoubleComplex* beta,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+/* -------------------------------------------------------------------- */
+/* TRMM */
+cublasStatus_t CUBLASWINAPI cublasXtStrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const float* alpha,
+                                          const float* A,
+                                          size_t lda,
+                                          const float* B,
+                                          size_t ldb,
+                                          float* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtDtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const double* alpha,
+                                          const double* A,
+                                          size_t lda,
+                                          const double* B,
+                                          size_t ldb,
+                                          double* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtCtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuComplex* alpha,
+                                          const cuComplex* A,
+                                          size_t lda,
+                                          const cuComplex* B,
+                                          size_t ldb,
+                                          cuComplex* C,
+                                          size_t ldc);
+
+cublasStatus_t CUBLASWINAPI cublasXtZtrmm(cublasXtHandle_t handle,
+                                          cublasSideMode_t side,
+                                          cublasFillMode_t uplo,
+                                          cublasOperation_t trans,
+                                          cublasDiagType_t diag,
+                                          size_t m,
+                                          size_t n,
+                                          const cuDoubleComplex* alpha,
+                                          const cuDoubleComplex* A,
+                                          size_t lda,
+                                          const cuDoubleComplex* B,
+                                          size_t ldb,
+                                          cuDoubleComplex* C,
+                                          size_t ldc);
+
+#if defined(__cplusplus)
+}
+#endif /* __cplusplus */
+
+#endif /* !defined(CUBLAS_XT_H_) */