diff --git a/.gitattributes b/.gitattributes index 4999f2dc912ff7fdb6e979c8f0fe30763b58a17a..e22461f0813e36ecbdefda1e2da61216ba3b668d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -53,3 +53,5 @@ pythonProject/.venv/Lib/site-packages/functorch/_C.cp310-win_amd64.pyd filter=lf pythonProject/.venv/Lib/site-packages/huggingface_hub/inference/__pycache__/_client.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text pythonProject/.venv/Lib/site-packages/numpy/fft/_pocketfft_umath.cp310-win_amd64.pyd filter=lfs diff=lfs merge=lfs -text pythonProject/.venv/Lib/site-packages/numpy/random/_common.cp310-win_amd64.pyd filter=lfs diff=lfs merge=lfs -text +pythonProject/.venv/Lib/site-packages/numpy/_core/tests/__pycache__/test_regression.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text +pythonProject/.venv/Lib/site-packages/numpy.libs/msvcp140-263139962577ecda4cd9469ca360a746.dll filter=lfs diff=lfs merge=lfs -text diff --git a/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/DELVEWHEEL b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/DELVEWHEEL new file mode 100644 index 0000000000000000000000000000000000000000..a7123d80cd0c058b99ad6adb5596e01b6a164cb4 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/DELVEWHEEL @@ -0,0 +1,2 @@ +Version: 1.10.1 +Arguments: ['C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\build\\venv\\Scripts\\delvewheel', 'repair', '--add-path', 'C:/a/numpy/numpy/.openblas/lib', '-w', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\repaired_wheel', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\built_wheel\\numpy-2.2.6-cp310-cp310-win_amd64.whl'] diff --git a/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/INSTALLER b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/LICENSE.txt b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..80edcc24fb577360fc0bcd8d815aee91bc5cad84 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/LICENSE.txt @@ -0,0 +1,950 @@ +Copyright (c) 2005-2024, 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. 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+ + +[![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) +[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/numpy/numpy/badge)](https://securityscorecards.dev/viewer/?uri=github.com/numpy/numpy) + + +NumPy is the fundamental package for scientific computing with Python. + +- **Website:** https://www.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://www.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. diff --git a/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/RECORD b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..a954a4951efa856a98cb5940653ec3c1fdaae09f --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy-2.2.6.dist-info/RECORD @@ -0,0 +1,1518 @@ +../../Scripts/f2py.exe,sha256=NajvPazW0Bm9nhYX1U55RzBwUES8Ghk00WrkaE1Xo4A,108406 +../../Scripts/numpy-config.exe,sha256=xsDBgGKyZbv2PaqXjtaf9cu2PNqNyq53h7Vj1h0M0p4,108406 +numpy-2.2.6-cp310-cp310-win_amd64.whl,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +numpy-2.2.6.dist-info/DELVEWHEEL,sha256=aqEcjAAXewVMaI3h7J_WiImSkhX8Rj8Y-2Sc1zwnwe8,446 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0000000000000000000000000000000000000000..babb5a67f0796f132db824af0397f72f19251df3 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/hook-numpy.py @@ -0,0 +1,36 @@ +"""This hook should collect all binary files and any hidden modules that numpy +needs. + +Our (some-what inadequate) docs for writing PyInstaller hooks are kept here: +https://pyinstaller.readthedocs.io/en/stable/hooks.html + +""" +from PyInstaller.compat import is_conda, is_pure_conda +from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies + +# Collect all DLLs inside numpy's installation folder, dump them into built +# app's root. +binaries = collect_dynamic_libs("numpy", ".") + +# If using Conda without any non-conda virtual environment manager: +if is_pure_conda: + # Assume running the NumPy from Conda-forge and collect it's DLLs from the + # communal Conda bin directory. DLLs from NumPy's dependencies must also be + # collected to capture MKL, OpenBlas, OpenMP, etc. + from PyInstaller.utils.hooks import conda_support + datas = conda_support.collect_dynamic_libs("numpy", dependencies=True) + +# Submodules PyInstaller cannot detect. `_dtype_ctypes` is only imported +# from C and `_multiarray_tests` is used in tests (which are not packed). +hiddenimports = ['numpy._core._dtype_ctypes', 'numpy._core._multiarray_tests'] + +# Remove testing and building code and packages that are referenced throughout +# NumPy but are not really dependencies. +excludedimports = [ + "scipy", + "pytest", + "f2py", + "setuptools", + "distutils", + "numpy.distutils", +] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/hook-numpy.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/hook-numpy.pyi new file mode 100644 index 0000000000000000000000000000000000000000..2978b8be623b3dbdbaec791bb0f430ba0f2fb460 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/hook-numpy.pyi @@ -0,0 +1,13 @@ +from typing import Final + +# from `PyInstaller.compat` +is_conda: Final[bool] +is_pure_conda: Final[bool] + +# from `PyInstaller.utils.hooks` +def is_module_satisfies(requirements: str, version: None = None, version_attr: None = None) -> bool: ... + +binaries: Final[list[tuple[str, str]]] + +hiddenimports: Final[list[str]] +excludedimports: Final[list[str]] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__init__.py b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c4392946a64fd3746f010035c57f6dde9f71eac8 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__init__.py @@ -0,0 +1,16 @@ +from numpy.testing import IS_WASM, IS_EDITABLE +import pytest + + +if IS_WASM: + pytest.skip( + "WASM/Pyodide does not use or support Fortran", + allow_module_level=True + ) + + +if IS_EDITABLE: + pytest.skip( + "Editable install doesn't support tests with a compile step", + allow_module_level=True + ) diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e5937d33450cef87af229b61485b3d91eb337380 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/__init__.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/pyinstaller-smoke.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/pyinstaller-smoke.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2b714e0a2f0036c85a8982d8c36fda5678d8b1f4 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/pyinstaller-smoke.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/test_pyinstaller.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/test_pyinstaller.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6a4a8266ab78afaa43e78bce7f64a5288cf93c72 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/__pycache__/test_pyinstaller.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..99b8358ba04897cc0d2a534701648df742a733dc --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/pyinstaller-smoke.py @@ -0,0 +1,32 @@ +"""A crude *bit of everything* smoke test to verify PyInstaller compatibility. + +PyInstaller typically goes wrong by forgetting to package modules, extension +modules or shared libraries. This script should aim to touch as many of those +as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure +due to an uncollected resource. Missing resources are unlikely to lead to +arithmetic errors so there's generally no need to verify any calculation's +output - merely that it made it to the end OK. This script should not +explicitly import any of numpy's submodules as that gives PyInstaller undue +hints that those submodules exist and should be collected (accessing implicitly +loaded submodules is OK). + +""" +import numpy as np + +a = np.arange(1., 10.).reshape((3, 3)) % 5 +np.linalg.det(a) +a @ a +a @ a.T +np.linalg.inv(a) +np.sin(np.exp(a)) +np.linalg.svd(a) +np.linalg.eigh(a) + +np.unique(np.random.randint(0, 10, 100)) +np.sort(np.random.uniform(0, 10, 100)) + +np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8)) +np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum() +np.polynomial.Legendre([7, 8, 9]).roots() + +print("I made it!") diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py new file mode 100644 index 0000000000000000000000000000000000000000..d8d29ebbb94944c06fdf87962aecd50a6b4945d5 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_pyinstaller/tests/test_pyinstaller.py @@ -0,0 +1,35 @@ +import subprocess +from pathlib import Path + +import pytest + + +# PyInstaller has been very unproactive about replacing 'imp' with 'importlib'. +@pytest.mark.filterwarnings('ignore::DeprecationWarning') +# It also leaks io.BytesIO()s. +@pytest.mark.filterwarnings('ignore::ResourceWarning') +@pytest.mark.parametrize("mode", ["--onedir", "--onefile"]) +@pytest.mark.slow +def test_pyinstaller(mode, tmp_path): + """Compile and run pyinstaller-smoke.py using PyInstaller.""" + + pyinstaller_cli = pytest.importorskip("PyInstaller.__main__").run + + source = Path(__file__).with_name("pyinstaller-smoke.py").resolve() + args = [ + # Place all generated files in ``tmp_path``. + '--workpath', str(tmp_path / "build"), + '--distpath', str(tmp_path / "dist"), + '--specpath', str(tmp_path), + mode, + str(source), + ] + pyinstaller_cli(args) + + if mode == "--onefile": + exe = tmp_path / "dist" / source.stem + else: + exe = tmp_path / "dist" / source.stem / source.stem + + p = subprocess.run([str(exe)], check=True, stdout=subprocess.PIPE) + assert p.stdout.strip() == b"I made it!" diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/__init__.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..67ddb9badfd6bd9f3777ad24084bd539de0906c9 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/__init__.py @@ -0,0 +1,154 @@ +"""Private counterpart of ``numpy.typing``.""" + +from __future__ import annotations + +from ._nested_sequence import ( + _NestedSequence as _NestedSequence, +) +from ._nbit_base import ( + NBitBase as NBitBase, + _8Bit as _8Bit, + _16Bit as _16Bit, + _32Bit as _32Bit, + _64Bit as _64Bit, + _80Bit as _80Bit, + _96Bit as _96Bit, + _128Bit as _128Bit, + _256Bit as _256Bit, +) +from ._nbit import ( + _NBitByte as _NBitByte, + _NBitShort as _NBitShort, + _NBitIntC as _NBitIntC, + _NBitIntP as _NBitIntP, + _NBitInt as _NBitInt, + _NBitLong as _NBitLong, + _NBitLongLong as _NBitLongLong, + _NBitHalf as _NBitHalf, + _NBitSingle as _NBitSingle, + _NBitDouble as _NBitDouble, + _NBitLongDouble as _NBitLongDouble, +) +from ._char_codes import ( + _BoolCodes as _BoolCodes, + _UInt8Codes as _UInt8Codes, + _UInt16Codes as _UInt16Codes, + _UInt32Codes as _UInt32Codes, + _UInt64Codes as _UInt64Codes, + _Int8Codes as _Int8Codes, + _Int16Codes as _Int16Codes, + _Int32Codes as _Int32Codes, + _Int64Codes as _Int64Codes, + _Float16Codes as _Float16Codes, + _Float32Codes as _Float32Codes, + _Float64Codes as _Float64Codes, + _Complex64Codes as _Complex64Codes, + _Complex128Codes as _Complex128Codes, + _ByteCodes as _ByteCodes, + _ShortCodes as _ShortCodes, + _IntCCodes as _IntCCodes, + _IntPCodes as _IntPCodes, + _IntCodes as _IntCodes, + _LongCodes as _LongCodes, + _LongLongCodes as _LongLongCodes, + _UByteCodes as _UByteCodes, + _UShortCodes as _UShortCodes, + _UIntCCodes as _UIntCCodes, + _UIntPCodes as _UIntPCodes, + _UIntCodes as _UIntCodes, + _ULongCodes as _ULongCodes, + _ULongLongCodes as _ULongLongCodes, + _HalfCodes as _HalfCodes, + _SingleCodes as _SingleCodes, + _DoubleCodes as _DoubleCodes, + _LongDoubleCodes as _LongDoubleCodes, + _CSingleCodes as _CSingleCodes, + _CDoubleCodes as _CDoubleCodes, + _CLongDoubleCodes as _CLongDoubleCodes, + _DT64Codes as _DT64Codes, + _TD64Codes as _TD64Codes, + _StrCodes as _StrCodes, + _BytesCodes as _BytesCodes, + _VoidCodes as _VoidCodes, + _ObjectCodes as _ObjectCodes, + _StringCodes as _StringCodes, + _UnsignedIntegerCodes as _UnsignedIntegerCodes, + _SignedIntegerCodes as _SignedIntegerCodes, + _IntegerCodes as _IntegerCodes, + _FloatingCodes as _FloatingCodes, + _ComplexFloatingCodes as _ComplexFloatingCodes, + _InexactCodes as _InexactCodes, + _NumberCodes as _NumberCodes, + _CharacterCodes as _CharacterCodes, + _FlexibleCodes as _FlexibleCodes, + _GenericCodes as _GenericCodes, +) +from ._scalars import ( + _CharLike_co as _CharLike_co, + _BoolLike_co as _BoolLike_co, + _UIntLike_co as _UIntLike_co, + _IntLike_co as _IntLike_co, + _FloatLike_co as _FloatLike_co, + _ComplexLike_co as _ComplexLike_co, + _TD64Like_co as _TD64Like_co, + _NumberLike_co as _NumberLike_co, + _ScalarLike_co as _ScalarLike_co, + _VoidLike_co as _VoidLike_co, +) +from ._shape import ( + _Shape as _Shape, + _ShapeLike as _ShapeLike, +) +from ._dtype_like import ( + DTypeLike as DTypeLike, + _DTypeLike as _DTypeLike, + _SupportsDType as _SupportsDType, + _VoidDTypeLike as _VoidDTypeLike, + _DTypeLikeBool as _DTypeLikeBool, + _DTypeLikeUInt as _DTypeLikeUInt, + _DTypeLikeInt as _DTypeLikeInt, + _DTypeLikeFloat as _DTypeLikeFloat, + _DTypeLikeComplex as _DTypeLikeComplex, + _DTypeLikeTD64 as _DTypeLikeTD64, + _DTypeLikeDT64 as _DTypeLikeDT64, + _DTypeLikeObject as _DTypeLikeObject, + _DTypeLikeVoid as _DTypeLikeVoid, + _DTypeLikeStr as _DTypeLikeStr, + _DTypeLikeBytes as _DTypeLikeBytes, + _DTypeLikeComplex_co as _DTypeLikeComplex_co, +) +from ._array_like import ( + NDArray as NDArray, + ArrayLike as ArrayLike, + _ArrayLike as _ArrayLike, + _ArrayLikeInt as _ArrayLikeInt, + _ArrayLikeBool_co as _ArrayLikeBool_co, + _ArrayLikeUInt_co as _ArrayLikeUInt_co, + _ArrayLikeInt_co as _ArrayLikeInt_co, + _ArrayLikeFloat_co as _ArrayLikeFloat_co, + _ArrayLikeFloat64_co as _ArrayLikeFloat64_co, + _ArrayLikeComplex_co as _ArrayLikeComplex_co, + _ArrayLikeComplex128_co as _ArrayLikeComplex128_co, + _ArrayLikeNumber_co as _ArrayLikeNumber_co, + _ArrayLikeTD64_co as _ArrayLikeTD64_co, + _ArrayLikeDT64_co as _ArrayLikeDT64_co, + _ArrayLikeObject_co as _ArrayLikeObject_co, + _ArrayLikeVoid_co as _ArrayLikeVoid_co, + _ArrayLikeStr_co as _ArrayLikeStr_co, + _ArrayLikeBytes_co as _ArrayLikeBytes_co, + _ArrayLikeString_co as _ArrayLikeString_co, + _ArrayLikeAnyString_co as _ArrayLikeAnyString_co, + _ArrayLikeUnknown as _ArrayLikeUnknown, + _FiniteNestedSequence as _FiniteNestedSequence, + _SupportsArray as _SupportsArray, + _SupportsArrayFunc as _SupportsArrayFunc, + _UnknownType as _UnknownType, +) + +from ._ufunc import ( + _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1, + _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1, + _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2, + _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2, + _GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1, +) diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_typing/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..081696c1b148619f8d4de663ba972308ea561601 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_typing/__pycache__/__init__.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/__pycache__/_add_docstring.cpython-310.pyc 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0000000000000000000000000000000000000000..b12ad5afe9060c1338dcb9c89642d9acd62c8ebf --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_add_docstring.py @@ -0,0 +1,153 @@ +"""A module for creating docstrings for sphinx ``data`` domains.""" + +import re +import textwrap + +from ._array_like import NDArray + +_docstrings_list = [] + + +def add_newdoc(name: str, value: str, doc: str) -> None: + """Append ``_docstrings_list`` with a docstring for `name`. + + Parameters + ---------- + name : str + The name of the object. + value : str + A string-representation of the object. + doc : str + The docstring of the object. + + """ + _docstrings_list.append((name, value, doc)) + + +def _parse_docstrings() -> str: + """Convert all docstrings in ``_docstrings_list`` into a single + sphinx-legible text block. + + """ + type_list_ret = [] + for name, value, doc in _docstrings_list: + s = textwrap.dedent(doc).replace("\n", "\n ") + + # Replace sections by rubrics + lines = s.split("\n") + new_lines = [] + indent = "" + for line in lines: + m = re.match(r'^(\s+)[-=]+\s*$', line) + if m and new_lines: + prev = textwrap.dedent(new_lines.pop()) + if prev == "Examples": + indent = "" + new_lines.append(f'{m.group(1)}.. rubric:: {prev}') + else: + indent = 4 * " " + new_lines.append(f'{m.group(1)}.. admonition:: {prev}') + new_lines.append("") + else: + new_lines.append(f"{indent}{line}") + + s = "\n".join(new_lines) + s_block = f""".. data:: {name}\n :value: {value}\n {s}""" + type_list_ret.append(s_block) + return "\n".join(type_list_ret) + + +add_newdoc('ArrayLike', 'typing.Union[...]', + """ + A `~typing.Union` representing objects that can be coerced + into an `~numpy.ndarray`. + + Among others this includes the likes of: + + * Scalars. + * (Nested) sequences. + * Objects implementing the `~class.__array__` protocol. + + .. versionadded:: 1.20 + + See Also + -------- + :term:`array_like`: + Any scalar or sequence that can be interpreted as an ndarray. + + Examples + -------- + .. code-block:: python + + >>> import numpy as np + >>> import numpy.typing as npt + + >>> def as_array(a: npt.ArrayLike) -> np.ndarray: + ... return np.array(a) + + """) + +add_newdoc('DTypeLike', 'typing.Union[...]', + """ + A `~typing.Union` representing objects that can be coerced + into a `~numpy.dtype`. + + Among others this includes the likes of: + + * :class:`type` objects. + * Character codes or the names of :class:`type` objects. + * Objects with the ``.dtype`` attribute. + + .. versionadded:: 1.20 + + See Also + -------- + :ref:`Specifying and constructing data types ` + A comprehensive overview of all objects that can be coerced + into data types. + + Examples + -------- + .. code-block:: python + + >>> import numpy as np + >>> import numpy.typing as npt + + >>> def as_dtype(d: npt.DTypeLike) -> np.dtype: + ... return np.dtype(d) + + """) + +add_newdoc('NDArray', repr(NDArray), + """ + A `np.ndarray[tuple[int, ...], np.dtype[+ScalarType]] ` + type alias :term:`generic ` w.r.t. its + `dtype.type `. + + Can be used during runtime for typing arrays with a given dtype + and unspecified shape. + + .. versionadded:: 1.21 + + Examples + -------- + .. code-block:: python + + >>> import numpy as np + >>> import numpy.typing as npt + + >>> print(npt.NDArray) + numpy.ndarray[tuple[int, ...], numpy.dtype[+_ScalarType_co]] + + >>> print(npt.NDArray[np.float64]) + numpy.ndarray[tuple[int, ...], numpy.dtype[numpy.float64]] + + >>> NDArrayInt = npt.NDArray[np.int_] + >>> a: NDArrayInt = np.arange(10) + + >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]: + ... return np.array(a) + + """) + +_docstrings = _parse_docstrings() diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_array_like.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_array_like.py new file mode 100644 index 0000000000000000000000000000000000000000..9db1fe46be56070dae20ee7d0bc078580be0ae28 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_array_like.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import sys +from collections.abc import Collection, Callable, Sequence +from typing import Any, Protocol, TypeAlias, TypeVar, runtime_checkable, TYPE_CHECKING + +import numpy as np +from numpy import ( + ndarray, + dtype, + generic, + unsignedinteger, + integer, + floating, + complexfloating, + number, + timedelta64, + datetime64, + object_, + void, + str_, + bytes_, +) +from ._nbit_base import _32Bit, _64Bit +from ._nested_sequence import _NestedSequence +from ._shape import _Shape + +if TYPE_CHECKING: + StringDType = np.dtypes.StringDType +else: + # at runtime outside of type checking importing this from numpy.dtypes + # would lead to a circular import + from numpy._core.multiarray import StringDType + +_T = TypeVar("_T") +_ScalarType = TypeVar("_ScalarType", bound=generic) +_ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True) +_DType = TypeVar("_DType", bound=dtype[Any]) +_DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any]) + +NDArray: TypeAlias = ndarray[_Shape, dtype[_ScalarType_co]] + +# The `_SupportsArray` protocol only cares about the default dtype +# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned +# array. +# Concrete implementations of the protocol are responsible for adding +# any and all remaining overloads +@runtime_checkable +class _SupportsArray(Protocol[_DType_co]): + def __array__(self) -> ndarray[Any, _DType_co]: ... + + +@runtime_checkable +class _SupportsArrayFunc(Protocol): + """A protocol class representing `~class.__array_function__`.""" + def __array_function__( + self, + func: Callable[..., Any], + types: Collection[type[Any]], + args: tuple[Any, ...], + kwargs: dict[str, Any], + ) -> object: ... + + +# TODO: Wait until mypy supports recursive objects in combination with typevars +_FiniteNestedSequence: TypeAlias = ( + _T + | Sequence[_T] + | Sequence[Sequence[_T]] + | Sequence[Sequence[Sequence[_T]]] + | Sequence[Sequence[Sequence[Sequence[_T]]]] +) + +# A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic` +_ArrayLike: TypeAlias = ( + _SupportsArray[dtype[_ScalarType]] + | _NestedSequence[_SupportsArray[dtype[_ScalarType]]] +) + +# A union representing array-like objects; consists of two typevars: +# One representing types that can be parametrized w.r.t. `np.dtype` +# and another one for the rest +_DualArrayLike: TypeAlias = ( + _SupportsArray[_DType] + | _NestedSequence[_SupportsArray[_DType]] + | _T + | _NestedSequence[_T] +) + +if sys.version_info >= (3, 12): + from collections.abc import Buffer as _Buffer +else: + @runtime_checkable + class _Buffer(Protocol): + def __buffer__(self, flags: int, /) -> memoryview: ... + +ArrayLike: TypeAlias = _Buffer | _DualArrayLike[ + dtype[Any], + bool | int | float | complex | str | bytes, +] + +# `ArrayLike_co`: array-like objects that can be coerced into `X` +# given the casting rules `same_kind` +_ArrayLikeBool_co: TypeAlias = _DualArrayLike[ + dtype[np.bool], + bool, +] +_ArrayLikeUInt_co: TypeAlias = _DualArrayLike[ + dtype[np.bool] | dtype[unsignedinteger[Any]], + bool, +] +_ArrayLikeInt_co: TypeAlias = _DualArrayLike[ + dtype[np.bool] | dtype[integer[Any]], + bool | int, +] +_ArrayLikeFloat_co: TypeAlias = _DualArrayLike[ + dtype[np.bool] | dtype[integer[Any]] | dtype[floating[Any]], + bool | int | float, +] +_ArrayLikeComplex_co: TypeAlias = _DualArrayLike[ + ( + dtype[np.bool] + | dtype[integer[Any]] + | dtype[floating[Any]] + | dtype[complexfloating[Any, Any]] + ), + bool | int | float | complex, +] +_ArrayLikeNumber_co: TypeAlias = _DualArrayLike[ + dtype[np.bool] | dtype[number[Any]], + bool | int | float | complex, +] +_ArrayLikeTD64_co: TypeAlias = _DualArrayLike[ + dtype[np.bool] | dtype[integer[Any]] | dtype[timedelta64], + bool | int, +] +_ArrayLikeDT64_co: TypeAlias = ( + _SupportsArray[dtype[datetime64]] + | _NestedSequence[_SupportsArray[dtype[datetime64]]] +) +_ArrayLikeObject_co: TypeAlias = ( + _SupportsArray[dtype[object_]] + | _NestedSequence[_SupportsArray[dtype[object_]]] +) + +_ArrayLikeVoid_co: TypeAlias = ( + _SupportsArray[dtype[void]] + | _NestedSequence[_SupportsArray[dtype[void]]] +) +_ArrayLikeStr_co: TypeAlias = _DualArrayLike[ + dtype[str_], + str, +] +_ArrayLikeBytes_co: TypeAlias = _DualArrayLike[ + dtype[bytes_], + bytes, +] +_ArrayLikeString_co: TypeAlias = _DualArrayLike[ + StringDType, + str +] +_ArrayLikeAnyString_co: TypeAlias = ( + _ArrayLikeStr_co | + _ArrayLikeBytes_co | + _ArrayLikeString_co +) + +__Float64_co: TypeAlias = np.floating[_64Bit] | np.float32 | np.float16 | np.integer | np.bool +__Complex128_co: TypeAlias = np.number[_64Bit] | np.number[_32Bit] | np.float16 | np.integer | np.bool +_ArrayLikeFloat64_co: TypeAlias = _DualArrayLike[dtype[__Float64_co], float | int] +_ArrayLikeComplex128_co: TypeAlias = _DualArrayLike[dtype[__Complex128_co], complex | float | int] + +# NOTE: This includes `builtins.bool`, but not `numpy.bool`. +_ArrayLikeInt: TypeAlias = _DualArrayLike[ + dtype[integer[Any]], + int, +] + +# Extra ArrayLike type so that pyright can deal with NDArray[Any] +# Used as the first overload, should only match NDArray[Any], +# not any actual types. +# https://github.com/numpy/numpy/pull/22193 +if sys.version_info >= (3, 11): + from typing import Never as _UnknownType +else: + from typing import NoReturn as _UnknownType + + +_ArrayLikeUnknown: TypeAlias = _DualArrayLike[ + dtype[_UnknownType], + _UnknownType, +] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_callable.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_callable.pyi new file mode 100644 index 0000000000000000000000000000000000000000..dee99ba1c94bba5dc1eb65ee7e348bc78b5398b0 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_callable.pyi @@ -0,0 +1,365 @@ +""" +A module with various ``typing.Protocol`` subclasses that implement +the ``__call__`` magic method. + +See the `Mypy documentation`_ on protocols for more details. + +.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols + +""" + +from typing import ( + TypeAlias, + TypeVar, + final, + overload, + Any, + NoReturn, + Protocol, + type_check_only, +) + +import numpy as np +from numpy import ( + generic, + number, + integer, + unsignedinteger, + signedinteger, + int8, + int_, + floating, + float64, + complexfloating, + complex128, +) +from ._nbit import _NBitInt +from ._scalars import ( + _BoolLike_co, + _IntLike_co, + _NumberLike_co, +) +from . import NBitBase +from ._array_like import NDArray +from ._nested_sequence import _NestedSequence + +_T1 = TypeVar("_T1") +_T2 = TypeVar("_T2") +_T1_contra = TypeVar("_T1_contra", contravariant=True) +_T2_contra = TypeVar("_T2_contra", contravariant=True) + +_2Tuple: TypeAlias = tuple[_T1, _T1] + +_NBit1 = TypeVar("_NBit1", bound=NBitBase) +_NBit2 = TypeVar("_NBit2", bound=NBitBase) + +_IntType = TypeVar("_IntType", bound=integer[Any]) +_FloatType = TypeVar("_FloatType", bound=floating[Any]) +_NumberType = TypeVar("_NumberType", bound=number[Any]) +_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number[Any]) +_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic) + +@type_check_only +class _BoolOp(Protocol[_GenericType_co]): + @overload + def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +@type_check_only +class _BoolBitOp(Protocol[_GenericType_co]): + @overload + def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: _IntType, /) -> _IntType: ... + +@type_check_only +class _BoolSub(Protocol): + # Note that `other: bool` is absent here + @overload + def __call__(self, other: bool, /) -> NoReturn: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +@type_check_only +class _BoolTrueDiv(Protocol): + @overload + def __call__(self, other: float | _IntLike_co, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: _NumberType, /) -> _NumberType: ... + +@type_check_only +class _BoolMod(Protocol): + @overload + def __call__(self, other: _BoolLike_co, /) -> int8: ... + @overload # platform dependent + def __call__(self, other: int, /) -> int_: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: _IntType, /) -> _IntType: ... + @overload + def __call__(self, other: _FloatType, /) -> _FloatType: ... + +@type_check_only +class _BoolDivMod(Protocol): + @overload + def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ... + @overload # platform dependent + def __call__(self, other: int, /) -> _2Tuple[int_]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[np.float64]: ... + @overload + def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ... + @overload + def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ... + +@type_check_only +class _IntTrueDiv(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> floating[_NBit1] | floating[_NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1] | float64: ... + @overload + def __call__( + self, other: complex, / + ) -> complexfloating[_NBit1, _NBit1] | complex128: ... + @overload + def __call__( + self, other: integer[_NBit2], / + ) -> floating[_NBit1] | floating[_NBit2]: ... + +@type_check_only +class _UnsignedIntOp(Protocol[_NBit1]): + # NOTE: `uint64 + signedinteger -> float64` + @overload + def __call__(self, other: int, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: unsignedinteger[_NBit2], /) -> unsignedinteger[_NBit1] | unsignedinteger[_NBit2]: ... + @overload + def __call__(self, other: signedinteger, /) -> Any: ... + +@type_check_only +class _UnsignedIntBitOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[Any]: ... + @overload + def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> unsignedinteger[_NBit1] | unsignedinteger[_NBit2]: ... + +@type_check_only +class _UnsignedIntMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ... + @overload + def __call__(self, other: int | signedinteger[Any], /) -> Any: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1] | float64: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> unsignedinteger[_NBit1] | unsignedinteger[_NBit2]: ... + +@type_check_only +class _UnsignedIntDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... + @overload + def __call__(self, other: int | signedinteger[Any], /) -> _2Tuple[Any]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1]] | _2Tuple[float64]: ... + @overload + def __call__( + self, other: unsignedinteger[_NBit2], / + ) -> _2Tuple[unsignedinteger[_NBit1]] | _2Tuple[unsignedinteger[_NBit2]]: ... + +@type_check_only +class _SignedIntOp(Protocol[_NBit1]): + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: float, /) -> float64: ... + @overload + def __call__(self, other: complex, /) -> complex128: ... + @overload + def __call__(self, other: signedinteger[_NBit2], /) -> signedinteger[_NBit1] | signedinteger[_NBit2]: ... + +@type_check_only +class _SignedIntBitOp(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1] | int_: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], / + ) -> signedinteger[_NBit1] | signedinteger[_NBit2]: ... + +@type_check_only +class _SignedIntMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> signedinteger[_NBit1] | int_: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1] | float64: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], / + ) -> signedinteger[_NBit1] | signedinteger[_NBit2]: ... + +@type_check_only +class _SignedIntDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ... + @overload + def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1]] | _2Tuple[int_]: ... + @overload + def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1]] | _2Tuple[float64]: ... + @overload + def __call__( + self, other: signedinteger[_NBit2], / + ) -> _2Tuple[signedinteger[_NBit1]] | _2Tuple[signedinteger[_NBit2]]: ... + +@type_check_only +class _FloatOp(Protocol[_NBit1]): + @overload + def __call__(self, other: int, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1] | float64: ... + @overload + def __call__( + self, other: complex, / + ) -> complexfloating[_NBit1, _NBit1] | complex128: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> floating[_NBit1] | floating[_NBit2]: ... + +@type_check_only +class _FloatMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> floating[_NBit1]: ... + @overload + def __call__(self, other: int, /) -> floating[_NBit1] | floating[_NBitInt]: ... + @overload + def __call__(self, other: float, /) -> floating[_NBit1] | float64: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> floating[_NBit1] | floating[_NBit2]: ... + +class _FloatDivMod(Protocol[_NBit1]): + @overload + def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ... + @overload + def __call__( + self, other: int, / + ) -> _2Tuple[floating[_NBit1]] | _2Tuple[floating[_NBitInt]]: ... + @overload + def __call__( + self, other: float, / + ) -> _2Tuple[floating[_NBit1]] | _2Tuple[float64]: ... + @overload + def __call__( + self, other: integer[_NBit2] | floating[_NBit2], / + ) -> _2Tuple[floating[_NBit1]] | _2Tuple[floating[_NBit2]]: ... + +@type_check_only +class _NumberOp(Protocol): + def __call__(self, other: _NumberLike_co, /) -> Any: ... + +@final +@type_check_only +class _SupportsLT(Protocol): + def __lt__(self, other: Any, /) -> Any: ... + +@final +@type_check_only +class _SupportsLE(Protocol): + def __le__(self, other: Any, /) -> Any: ... + +@final +@type_check_only +class _SupportsGT(Protocol): + def __gt__(self, other: Any, /) -> Any: ... + +@final +@type_check_only +class _SupportsGE(Protocol): + def __ge__(self, other: Any, /) -> Any: ... + +@final +@type_check_only +class _ComparisonOpLT(Protocol[_T1_contra, _T2_contra]): + @overload + def __call__(self, other: _T1_contra, /) -> np.bool: ... + @overload + def __call__(self, other: _T2_contra, /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _NestedSequence[_SupportsGT], /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _SupportsGT, /) -> np.bool: ... + +@final +@type_check_only +class _ComparisonOpLE(Protocol[_T1_contra, _T2_contra]): + @overload + def __call__(self, other: _T1_contra, /) -> np.bool: ... + @overload + def __call__(self, other: _T2_contra, /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _NestedSequence[_SupportsGE], /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _SupportsGE, /) -> np.bool: ... + +@final +@type_check_only +class _ComparisonOpGT(Protocol[_T1_contra, _T2_contra]): + @overload + def __call__(self, other: _T1_contra, /) -> np.bool: ... + @overload + def __call__(self, other: _T2_contra, /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _NestedSequence[_SupportsLT], /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _SupportsLT, /) -> np.bool: ... + +@final +@type_check_only +class _ComparisonOpGE(Protocol[_T1_contra, _T2_contra]): + @overload + def __call__(self, other: _T1_contra, /) -> np.bool: ... + @overload + def __call__(self, other: _T2_contra, /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _NestedSequence[_SupportsGT], /) -> NDArray[np.bool]: ... + @overload + def __call__(self, other: _SupportsGT, /) -> np.bool: ... diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_char_codes.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_char_codes.py new file mode 100644 index 0000000000000000000000000000000000000000..3560527efbeea24d2909aa38eeac3d1c23f258d9 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_char_codes.py @@ -0,0 +1,214 @@ +from typing import Literal + +_BoolCodes = Literal[ + "bool", "bool_", + "?", "|?", "=?", "?", + "b1", "|b1", "=b1", "b1", +] # fmt: skip + +_UInt8Codes = Literal["uint8", "u1", "|u1", "=u1", "u1"] +_UInt16Codes = Literal["uint16", "u2", "|u2", "=u2", "u2"] +_UInt32Codes = Literal["uint32", "u4", "|u4", "=u4", "u4"] +_UInt64Codes = Literal["uint64", "u8", "|u8", "=u8", "u8"] + +_Int8Codes = Literal["int8", "i1", "|i1", "=i1", "i1"] +_Int16Codes = Literal["int16", "i2", "|i2", "=i2", "i2"] +_Int32Codes = Literal["int32", "i4", "|i4", "=i4", "i4"] +_Int64Codes = Literal["int64", "i8", "|i8", "=i8", "i8"] + +_Float16Codes = Literal["float16", "f2", "|f2", "=f2", "f2"] +_Float32Codes = Literal["float32", "f4", "|f4", "=f4", "f4"] +_Float64Codes = Literal["float64", "f8", "|f8", "=f8", "f8"] + +_Complex64Codes = Literal["complex64", "c8", "|c8", "=c8", "c8"] +_Complex128Codes = Literal["complex128", "c16", "|c16", "=c16", "c16"] + +_ByteCodes = Literal["byte", "b", "|b", "=b", "b"] +_ShortCodes = Literal["short", "h", "|h", "=h", "h"] +_IntCCodes = Literal["intc", "i", "|i", "=i", "i"] +_IntPCodes = Literal["intp", "int", "int_", "n", "|n", "=n", "n"] +_LongCodes = Literal["long", "l", "|l", "=l", "l"] +_IntCodes = _IntPCodes +_LongLongCodes = Literal["longlong", "q", "|q", "=q", "q"] + +_UByteCodes = Literal["ubyte", "B", "|B", "=B", "B"] +_UShortCodes = Literal["ushort", "H", "|H", "=H", "H"] +_UIntCCodes = Literal["uintc", "I", "|I", "=I", "I"] +_UIntPCodes = Literal["uintp", "uint", "N", "|N", "=N", "N"] +_ULongCodes = Literal["ulong", "L", "|L", "=L", "L"] +_UIntCodes = _UIntPCodes +_ULongLongCodes = Literal["ulonglong", "Q", "|Q", "=Q", "Q"] + +_HalfCodes = Literal["half", "e", "|e", "=e", "e"] +_SingleCodes = Literal["single", "f", "|f", "=f", "f"] +_DoubleCodes = Literal["double", "float", "d", "|d", "=d", "d"] +_LongDoubleCodes = Literal["longdouble", "g", "|g", "=g", "g"] + +_CSingleCodes = Literal["csingle", "F", "|F", "=F", "F"] +_CDoubleCodes = Literal["cdouble", "complex", "D", "|D", "=D", "D"] +_CLongDoubleCodes = Literal["clongdouble", "G", "|G", "=G", "G"] + +_StrCodes = Literal["str", "str_", "unicode", "U", "|U", "=U", "U"] +_BytesCodes = Literal["bytes", "bytes_", "S", "|S", "=S", "S"] +_VoidCodes = Literal["void", "V", "|V", "=V", "V"] +_ObjectCodes = Literal["object", "object_", "O", "|O", "=O", "O"] + +_DT64Codes = Literal[ + "datetime64", "|datetime64", "=datetime64", + "datetime64", + "datetime64[Y]", "|datetime64[Y]", "=datetime64[Y]", + "datetime64[Y]", + "datetime64[M]", "|datetime64[M]", "=datetime64[M]", + "datetime64[M]", + "datetime64[W]", "|datetime64[W]", "=datetime64[W]", + "datetime64[W]", + "datetime64[D]", "|datetime64[D]", "=datetime64[D]", + "datetime64[D]", + "datetime64[h]", "|datetime64[h]", "=datetime64[h]", + "datetime64[h]", + "datetime64[m]", "|datetime64[m]", "=datetime64[m]", + "datetime64[m]", + "datetime64[s]", "|datetime64[s]", "=datetime64[s]", + "datetime64[s]", + "datetime64[ms]", "|datetime64[ms]", "=datetime64[ms]", + "datetime64[ms]", + "datetime64[us]", "|datetime64[us]", "=datetime64[us]", + "datetime64[us]", + "datetime64[ns]", "|datetime64[ns]", "=datetime64[ns]", + "datetime64[ns]", + "datetime64[ps]", "|datetime64[ps]", "=datetime64[ps]", + "datetime64[ps]", + "datetime64[fs]", "|datetime64[fs]", "=datetime64[fs]", + "datetime64[fs]", + "datetime64[as]", "|datetime64[as]", "=datetime64[as]", + "datetime64[as]", + "M", "|M", "=M", "M", + "M8", "|M8", "=M8", "M8", + "M8[Y]", "|M8[Y]", "=M8[Y]", "M8[Y]", + "M8[M]", "|M8[M]", "=M8[M]", "M8[M]", + "M8[W]", "|M8[W]", "=M8[W]", "M8[W]", + "M8[D]", "|M8[D]", "=M8[D]", "M8[D]", + "M8[h]", "|M8[h]", "=M8[h]", "M8[h]", + "M8[m]", "|M8[m]", "=M8[m]", "M8[m]", + "M8[s]", "|M8[s]", "=M8[s]", "M8[s]", + "M8[ms]", "|M8[ms]", "=M8[ms]", "M8[ms]", + "M8[us]", "|M8[us]", "=M8[us]", "M8[us]", + "M8[ns]", "|M8[ns]", "=M8[ns]", "M8[ns]", + "M8[ps]", "|M8[ps]", "=M8[ps]", "M8[ps]", + "M8[fs]", "|M8[fs]", "=M8[fs]", "M8[fs]", + "M8[as]", "|M8[as]", "=M8[as]", "M8[as]", +] +_TD64Codes = Literal[ + "timedelta64", "|timedelta64", "=timedelta64", + "timedelta64", + "timedelta64[Y]", "|timedelta64[Y]", "=timedelta64[Y]", + "timedelta64[Y]", + "timedelta64[M]", "|timedelta64[M]", "=timedelta64[M]", + "timedelta64[M]", + "timedelta64[W]", "|timedelta64[W]", "=timedelta64[W]", + "timedelta64[W]", + "timedelta64[D]", "|timedelta64[D]", "=timedelta64[D]", + "timedelta64[D]", + "timedelta64[h]", "|timedelta64[h]", "=timedelta64[h]", + "timedelta64[h]", + "timedelta64[m]", "|timedelta64[m]", "=timedelta64[m]", + "timedelta64[m]", + "timedelta64[s]", "|timedelta64[s]", "=timedelta64[s]", + "timedelta64[s]", + "timedelta64[ms]", "|timedelta64[ms]", "=timedelta64[ms]", + "timedelta64[ms]", + "timedelta64[us]", "|timedelta64[us]", "=timedelta64[us]", + "timedelta64[us]", + "timedelta64[ns]", "|timedelta64[ns]", "=timedelta64[ns]", + "timedelta64[ns]", + "timedelta64[ps]", "|timedelta64[ps]", "=timedelta64[ps]", + "timedelta64[ps]", + "timedelta64[fs]", "|timedelta64[fs]", "=timedelta64[fs]", + "timedelta64[fs]", + "timedelta64[as]", "|timedelta64[as]", "=timedelta64[as]", + "timedelta64[as]", + "m", "|m", "=m", "m", + "m8", "|m8", "=m8", "m8", + "m8[Y]", "|m8[Y]", "=m8[Y]", "m8[Y]", + "m8[M]", "|m8[M]", "=m8[M]", "m8[M]", + "m8[W]", "|m8[W]", "=m8[W]", "m8[W]", + "m8[D]", "|m8[D]", "=m8[D]", "m8[D]", + "m8[h]", "|m8[h]", "=m8[h]", "m8[h]", + "m8[m]", "|m8[m]", "=m8[m]", "m8[m]", + "m8[s]", "|m8[s]", "=m8[s]", "m8[s]", + "m8[ms]", "|m8[ms]", "=m8[ms]", "m8[ms]", + "m8[us]", "|m8[us]", "=m8[us]", "m8[us]", + "m8[ns]", "|m8[ns]", "=m8[ns]", "m8[ns]", + "m8[ps]", "|m8[ps]", "=m8[ps]", "m8[ps]", + "m8[fs]", "|m8[fs]", "=m8[fs]", "m8[fs]", + "m8[as]", "|m8[as]", "=m8[as]", "m8[as]", +] + +# NOTE: `StringDType' has no scalar type, and therefore has no name that can +# be passed to the `dtype` constructor +_StringCodes = Literal["T", "|T", "=T", "T"] + +# NOTE: Nested literals get flattened and de-duplicated at runtime, which isn't +# the case for a `Union` of `Literal`s. +# So even though they're equivalent when type-checking, they differ at runtime. +# Another advantage of nesting, is that they always have a "flat" +# `Literal.__args__`, which is a tuple of *literally* all its literal values. + +_UnsignedIntegerCodes = Literal[ + _UInt8Codes, + _UInt16Codes, + _UInt32Codes, + _UInt64Codes, + _UIntCodes, + _UByteCodes, + _UShortCodes, + _UIntCCodes, + _ULongCodes, + _ULongLongCodes, +] +_SignedIntegerCodes = Literal[ + _Int8Codes, + _Int16Codes, + _Int32Codes, + _Int64Codes, + _IntCodes, + _ByteCodes, + _ShortCodes, + _IntCCodes, + _LongCodes, + _LongLongCodes, +] +_FloatingCodes = Literal[ + _Float16Codes, + _Float32Codes, + _Float64Codes, + _LongDoubleCodes, + _HalfCodes, + _SingleCodes, + _DoubleCodes, + _LongDoubleCodes +] +_ComplexFloatingCodes = Literal[ + _Complex64Codes, + _Complex128Codes, + _CSingleCodes, + _CDoubleCodes, + _CLongDoubleCodes, +] +_IntegerCodes = Literal[_UnsignedIntegerCodes, _SignedIntegerCodes] +_InexactCodes = Literal[_FloatingCodes, _ComplexFloatingCodes] +_NumberCodes = Literal[_IntegerCodes, _InexactCodes] + +_CharacterCodes = Literal[_StrCodes, _BytesCodes] +_FlexibleCodes = Literal[_VoidCodes, _CharacterCodes] + +_GenericCodes = Literal[ + _BoolCodes, + _NumberCodes, + _FlexibleCodes, + _DT64Codes, + _TD64Codes, + _ObjectCodes, + # TODO: add `_StringCodes` once it has a scalar type + # _StringCodes, +] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_dtype_like.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_dtype_like.py new file mode 100644 index 0000000000000000000000000000000000000000..52753cbb227630af2dd42801fb48b003cbe21b0d --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_dtype_like.py @@ -0,0 +1,249 @@ +from collections.abc import Sequence # noqa: F811 +from typing import ( + Any, + TypeAlias, + TypeVar, + Protocol, + TypedDict, + runtime_checkable, +) + +import numpy as np + +from ._shape import _ShapeLike + +from ._char_codes import ( + _BoolCodes, + _UInt8Codes, + _UInt16Codes, + _UInt32Codes, + _UInt64Codes, + _Int8Codes, + _Int16Codes, + _Int32Codes, + _Int64Codes, + _Float16Codes, + _Float32Codes, + _Float64Codes, + _Complex64Codes, + _Complex128Codes, + _ByteCodes, + _ShortCodes, + _IntCCodes, + _LongCodes, + _LongLongCodes, + _IntPCodes, + _IntCodes, + _UByteCodes, + _UShortCodes, + _UIntCCodes, + _ULongCodes, + _ULongLongCodes, + _UIntPCodes, + _UIntCodes, + _HalfCodes, + _SingleCodes, + _DoubleCodes, + _LongDoubleCodes, + _CSingleCodes, + _CDoubleCodes, + _CLongDoubleCodes, + _DT64Codes, + _TD64Codes, + _StrCodes, + _BytesCodes, + _VoidCodes, + _ObjectCodes, +) + +_SCT = TypeVar("_SCT", bound=np.generic) +_DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any]) + +_DTypeLikeNested: TypeAlias = Any # TODO: wait for support for recursive types + + +# Mandatory keys +class _DTypeDictBase(TypedDict): + names: Sequence[str] + formats: Sequence[_DTypeLikeNested] + + +# Mandatory + optional keys +class _DTypeDict(_DTypeDictBase, total=False): + # Only `str` elements are usable as indexing aliases, + # but `titles` can in principle accept any object + offsets: Sequence[int] + titles: Sequence[Any] + itemsize: int + aligned: bool + + +# A protocol for anything with the dtype attribute +@runtime_checkable +class _SupportsDType(Protocol[_DType_co]): + @property + def dtype(self) -> _DType_co: ... + + +# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic` +_DTypeLike: TypeAlias = ( + np.dtype[_SCT] + | type[_SCT] + | _SupportsDType[np.dtype[_SCT]] +) + + +# Would create a dtype[np.void] +_VoidDTypeLike: TypeAlias = ( + # (flexible_dtype, itemsize) + tuple[_DTypeLikeNested, int] + # (fixed_dtype, shape) + | tuple[_DTypeLikeNested, _ShapeLike] + # [(field_name, field_dtype, field_shape), ...] + # + # The type here is quite broad because NumPy accepts quite a wide + # range of inputs inside the list; see the tests for some + # examples. + | list[Any] + # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ..., + # 'itemsize': ...} + | _DTypeDict + # (base_dtype, new_dtype) + | tuple[_DTypeLikeNested, _DTypeLikeNested] +) + +# Anything that can be coerced into numpy.dtype. +# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html +DTypeLike: TypeAlias = ( + np.dtype[Any] + # default data type (float64) + | None + # array-scalar types and generic types + | type[Any] # NOTE: We're stuck with `type[Any]` due to object dtypes + # anything with a dtype attribute + | _SupportsDType[np.dtype[Any]] + # character codes, type strings or comma-separated fields, e.g., 'float64' + | str + | _VoidDTypeLike +) + +# NOTE: while it is possible to provide the dtype as a dict of +# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`), +# this syntax is officially discouraged and +# therefore not included in the type-union defining `DTypeLike`. +# +# See https://github.com/numpy/numpy/issues/16891 for more details. + +# Aliases for commonly used dtype-like objects. +# Note that the precision of `np.number` subclasses is ignored herein. +_DTypeLikeBool: TypeAlias = ( + type[bool] + | type[np.bool] + | np.dtype[np.bool] + | _SupportsDType[np.dtype[np.bool]] + | _BoolCodes +) +_DTypeLikeUInt: TypeAlias = ( + type[np.unsignedinteger[Any]] + | np.dtype[np.unsignedinteger[Any]] + | _SupportsDType[np.dtype[np.unsignedinteger[Any]]] + | _UInt8Codes + | _UInt16Codes + | _UInt32Codes + | _UInt64Codes + | _UByteCodes + | _UShortCodes + | _UIntCCodes + | _LongCodes + | _ULongLongCodes + | _UIntPCodes + | _UIntCodes +) +_DTypeLikeInt: TypeAlias = ( + type[int] + | type[np.signedinteger[Any]] + | np.dtype[np.signedinteger[Any]] + | _SupportsDType[np.dtype[np.signedinteger[Any]]] + | _Int8Codes + | _Int16Codes + | _Int32Codes + | _Int64Codes + | _ByteCodes + | _ShortCodes + | _IntCCodes + | _LongCodes + | _LongLongCodes + | _IntPCodes + | _IntCodes +) +_DTypeLikeFloat: TypeAlias = ( + type[float] + | type[np.floating[Any]] + | np.dtype[np.floating[Any]] + | _SupportsDType[np.dtype[np.floating[Any]]] + | _Float16Codes + | _Float32Codes + | _Float64Codes + | _HalfCodes + | _SingleCodes + | _DoubleCodes + | _LongDoubleCodes +) +_DTypeLikeComplex: TypeAlias = ( + type[complex] + | type[np.complexfloating[Any]] + | np.dtype[np.complexfloating[Any]] + | _SupportsDType[np.dtype[np.complexfloating[Any]]] + | _Complex64Codes + | _Complex128Codes + | _CSingleCodes + | _CDoubleCodes + | _CLongDoubleCodes +) +_DTypeLikeDT64: TypeAlias = ( + type[np.timedelta64] + | np.dtype[np.timedelta64] + | _SupportsDType[np.dtype[np.timedelta64]] + | _TD64Codes +) +_DTypeLikeTD64: TypeAlias = ( + type[np.datetime64] + | np.dtype[np.datetime64] + | _SupportsDType[np.dtype[np.datetime64]] + | _DT64Codes +) +_DTypeLikeStr: TypeAlias = ( + type[str] + | type[np.str_] + | np.dtype[np.str_] + | _SupportsDType[np.dtype[np.str_]] + | _StrCodes +) +_DTypeLikeBytes: TypeAlias = ( + type[bytes] + | type[np.bytes_] + | np.dtype[np.bytes_] + | _SupportsDType[np.dtype[np.bytes_]] + | _BytesCodes +) +_DTypeLikeVoid: TypeAlias = ( + type[np.void] + | np.dtype[np.void] + | _SupportsDType[np.dtype[np.void]] + | _VoidCodes + | _VoidDTypeLike +) +_DTypeLikeObject: TypeAlias = ( + type + | np.dtype[np.object_] + | _SupportsDType[np.dtype[np.object_]] + | _ObjectCodes +) + +_DTypeLikeComplex_co: TypeAlias = ( + _DTypeLikeBool + | _DTypeLikeUInt + | _DTypeLikeInt + | _DTypeLikeFloat + | _DTypeLikeComplex +) diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_extended_precision.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_extended_precision.py new file mode 100644 index 0000000000000000000000000000000000000000..2af48891bc0fd4fda7bbc34b01e1eb2d5e7146f6 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_extended_precision.py @@ -0,0 +1,27 @@ +"""A module with platform-specific extended precision +`numpy.number` subclasses. + +The subclasses are defined here (instead of ``__init__.pyi``) such +that they can be imported conditionally via the numpy's mypy plugin. +""" + +import numpy as np +from . import ( + _80Bit, + _96Bit, + _128Bit, + _256Bit, +) + +uint128 = np.unsignedinteger[_128Bit] +uint256 = np.unsignedinteger[_256Bit] +int128 = np.signedinteger[_128Bit] +int256 = np.signedinteger[_256Bit] +float80 = np.floating[_80Bit] +float96 = np.floating[_96Bit] +float128 = np.floating[_128Bit] +float256 = np.floating[_256Bit] +complex160 = np.complexfloating[_80Bit, _80Bit] +complex192 = np.complexfloating[_96Bit, _96Bit] +complex256 = np.complexfloating[_128Bit, _128Bit] +complex512 = np.complexfloating[_256Bit, _256Bit] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit.py new file mode 100644 index 0000000000000000000000000000000000000000..cdba6a32f7e652ebd8e505fb3257fd017d3bb612 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit.py @@ -0,0 +1,19 @@ +"""A module with the precisions of platform-specific `~numpy.number`s.""" + +from typing import TypeAlias +from ._nbit_base import _8Bit, _16Bit, _32Bit, _64Bit, _96Bit, _128Bit + + +# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin +_NBitByte: TypeAlias = _8Bit +_NBitShort: TypeAlias = _16Bit +_NBitIntC: TypeAlias = _32Bit +_NBitIntP: TypeAlias = _32Bit | _64Bit +_NBitInt: TypeAlias = _NBitIntP +_NBitLong: TypeAlias = _32Bit | _64Bit +_NBitLongLong: TypeAlias = _64Bit + +_NBitHalf: TypeAlias = _16Bit +_NBitSingle: TypeAlias = _32Bit +_NBitDouble: TypeAlias = _64Bit +_NBitLongDouble: TypeAlias = _64Bit | _96Bit | _128Bit diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit_base.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit_base.py new file mode 100644 index 0000000000000000000000000000000000000000..92218f2ed844228c00fb5de7736af28aa071b59b --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nbit_base.py @@ -0,0 +1,100 @@ +"""A module with the precisions of generic `~numpy.number` types.""" +from .._utils import set_module +from typing import final + + +@final # Disallow the creation of arbitrary `NBitBase` subclasses +@set_module("numpy.typing") +class NBitBase: + """ + A type representing `numpy.number` precision during static type checking. + + Used exclusively for the purpose static type checking, `NBitBase` + represents the base of a hierarchical set of subclasses. + Each subsequent subclass is herein used for representing a lower level + of precision, *e.g.* ``64Bit > 32Bit > 16Bit``. + + .. versionadded:: 1.20 + + Examples + -------- + Below is a typical usage example: `NBitBase` is herein used for annotating + a function that takes a float and integer of arbitrary precision + as arguments and returns a new float of whichever precision is largest + (*e.g.* ``np.float16 + np.int64 -> np.float64``). + + .. code-block:: python + + >>> from __future__ import annotations + >>> from typing import TypeVar, TYPE_CHECKING + >>> import numpy as np + >>> import numpy.typing as npt + + >>> S = TypeVar("S", bound=npt.NBitBase) + >>> T = TypeVar("T", bound=npt.NBitBase) + + >>> def add(a: np.floating[S], b: np.integer[T]) -> np.floating[S | T]: + ... return a + b + + >>> a = np.float16() + >>> b = np.int64() + >>> out = add(a, b) + + >>> if TYPE_CHECKING: + ... reveal_locals() + ... # note: Revealed local types are: + ... # note: a: numpy.floating[numpy.typing._16Bit*] + ... # note: b: numpy.signedinteger[numpy.typing._64Bit*] + ... # note: out: numpy.floating[numpy.typing._64Bit*] + + """ + + def __init_subclass__(cls) -> None: + allowed_names = { + "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit", + "_64Bit", "_32Bit", "_16Bit", "_8Bit", + } + if cls.__name__ not in allowed_names: + raise TypeError('cannot inherit from final class "NBitBase"') + super().__init_subclass__() + +@final +@set_module("numpy._typing") +# Silence errors about subclassing a `@final`-decorated class +class _256Bit(NBitBase): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _128Bit(_256Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _96Bit(_128Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _80Bit(_96Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _64Bit(_80Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _32Bit(_64Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _16Bit(_32Bit): # type: ignore[misc] + pass + +@final +@set_module("numpy._typing") +class _8Bit(_16Bit): # type: ignore[misc] + pass diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nested_sequence.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nested_sequence.py new file mode 100644 index 0000000000000000000000000000000000000000..b6fb08d1c0057c07b15839d73b4f4402fccb3921 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_nested_sequence.py @@ -0,0 +1,89 @@ +"""A module containing the `_NestedSequence` protocol.""" + +from __future__ import annotations + +from typing import ( + Any, + TypeVar, + Protocol, + runtime_checkable, + TYPE_CHECKING, +) + +if TYPE_CHECKING: + from collections.abc import Iterator + +__all__ = ["_NestedSequence"] + +_T_co = TypeVar("_T_co", covariant=True) + + +@runtime_checkable +class _NestedSequence(Protocol[_T_co]): + """A protocol for representing nested sequences. + + Warning + ------- + `_NestedSequence` currently does not work in combination with typevars, + *e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``. + + See Also + -------- + collections.abc.Sequence + ABCs for read-only and mutable :term:`sequences`. + + Examples + -------- + .. code-block:: python + + >>> from __future__ import annotations + + >>> from typing import TYPE_CHECKING + >>> import numpy as np + >>> from numpy._typing import _NestedSequence + + >>> def get_dtype(seq: _NestedSequence[float]) -> np.dtype[np.float64]: + ... return np.asarray(seq).dtype + + >>> a = get_dtype([1.0]) + >>> b = get_dtype([[1.0]]) + >>> c = get_dtype([[[1.0]]]) + >>> d = get_dtype([[[[1.0]]]]) + + >>> if TYPE_CHECKING: + ... reveal_locals() + ... # note: Revealed local types are: + ... # note: a: numpy.dtype[numpy.floating[numpy._typing._64Bit]] + ... # note: b: numpy.dtype[numpy.floating[numpy._typing._64Bit]] + ... # note: c: numpy.dtype[numpy.floating[numpy._typing._64Bit]] + ... # note: d: numpy.dtype[numpy.floating[numpy._typing._64Bit]] + + """ + + def __len__(self, /) -> int: + """Implement ``len(self)``.""" + raise NotImplementedError + + def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]: + """Implement ``self[x]``.""" + raise NotImplementedError + + def __contains__(self, x: object, /) -> bool: + """Implement ``x in self``.""" + raise NotImplementedError + + def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: + """Implement ``iter(self)``.""" + raise NotImplementedError + + def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: + """Implement ``reversed(self)``.""" + raise NotImplementedError + + def count(self, value: Any, /) -> int: + """Return the number of occurrences of `value`.""" + raise NotImplementedError + + def index(self, value: Any, /) -> int: + """Return the first index of `value`.""" + raise NotImplementedError diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_scalars.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_scalars.py new file mode 100644 index 0000000000000000000000000000000000000000..fa9fab33198ad7718dba27f758218e0259df3234 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_scalars.py @@ -0,0 +1,27 @@ +from typing import Any, TypeAlias + +import numpy as np + +# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and +# `np.bytes_` are already subclasses of their builtin counterpart + +_CharLike_co: TypeAlias = str | bytes + +# The 6 `Like_co` type-aliases below represent all scalars that can be +# coerced into `` (with the casting rule `same_kind`) +_BoolLike_co: TypeAlias = bool | np.bool +_UIntLike_co: TypeAlias = np.unsignedinteger[Any] | _BoolLike_co +_IntLike_co: TypeAlias = int | np.integer[Any] | _BoolLike_co +_FloatLike_co: TypeAlias = float | np.floating[Any] | _IntLike_co +_ComplexLike_co: TypeAlias = ( + complex + | np.complexfloating[Any, Any] + | _FloatLike_co +) +_TD64Like_co: TypeAlias = np.timedelta64 | _IntLike_co + +_NumberLike_co: TypeAlias = int | float | complex | np.number[Any] | np.bool +_ScalarLike_co: TypeAlias = int | float | complex | str | bytes | np.generic + +# `_VoidLike_co` is technically not a scalar, but it's close enough +_VoidLike_co: TypeAlias = tuple[Any, ...] | np.void diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_shape.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_shape.py new file mode 100644 index 0000000000000000000000000000000000000000..e297e3480c5952bb46418b1b123cabe15a0350f6 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_shape.py @@ -0,0 +1,7 @@ +from collections.abc import Sequence +from typing import SupportsIndex, TypeAlias + +_Shape: TypeAlias = tuple[int, ...] + +# Anything that can be coerced to a shape tuple +_ShapeLike: TypeAlias = SupportsIndex | Sequence[SupportsIndex] diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.py b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.py new file mode 100644 index 0000000000000000000000000000000000000000..835b099c49b7be4e131467f79b618c5539e3ec44 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.py @@ -0,0 +1,7 @@ +from .. import ufunc + +_UFunc_Nin1_Nout1 = ufunc +_UFunc_Nin2_Nout1 = ufunc +_UFunc_Nin1_Nout2 = ufunc +_UFunc_Nin2_Nout2 = ufunc +_GUFunc_Nin2_Nout1 = ufunc diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3d1ae5837c68ee4675ab78c1fdd73143cc9b24ac --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_typing/_ufunc.pyi @@ -0,0 +1,942 @@ +"""A module with private type-check-only `numpy.ufunc` subclasses. + +The signatures of the ufuncs are too varied to reasonably type +with a single class. So instead, `ufunc` has been expanded into +four private subclasses, one for each combination of +`~ufunc.nin` and `~ufunc.nout`. +""" + +from typing import ( + Any, + Generic, + Literal, + NoReturn, + Protocol, + SupportsIndex, + TypeAlias, + TypedDict, + TypeVar, + overload, + type_check_only, +) + +from typing_extensions import LiteralString, Unpack + +import numpy as np +from numpy import _CastingKind, _OrderKACF, ufunc +from numpy.typing import NDArray + +from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co +from ._dtype_like import DTypeLike +from ._scalars import _ScalarLike_co +from ._shape import _ShapeLike + +_T = TypeVar("_T") +_2Tuple: TypeAlias = tuple[_T, _T] +_3Tuple: TypeAlias = tuple[_T, _T, _T] +_4Tuple: TypeAlias = tuple[_T, _T, _T, _T] + +_2PTuple: TypeAlias = tuple[_T, _T, Unpack[tuple[_T, ...]]] +_3PTuple: TypeAlias = tuple[_T, _T, _T, Unpack[tuple[_T, ...]]] +_4PTuple: TypeAlias = tuple[_T, _T, _T, _T, Unpack[tuple[_T, ...]]] + +_NTypes = TypeVar("_NTypes", bound=int, covariant=True) +_IDType = TypeVar("_IDType", covariant=True) +_NameType = TypeVar("_NameType", bound=LiteralString, covariant=True) +_Signature = TypeVar("_Signature", bound=LiteralString, covariant=True) + +_NIn = TypeVar("_NIn", bound=int, covariant=True) +_NOut = TypeVar("_NOut", bound=int, covariant=True) +_ReturnType_co = TypeVar("_ReturnType_co", covariant=True) +_ArrayType = TypeVar("_ArrayType", bound=np.ndarray[Any, Any]) + + +@type_check_only +class _SupportsArrayUFunc(Protocol): + def __array_ufunc__( + self, + ufunc: ufunc, + method: Literal["__call__", "reduce", "reduceat", "accumulate", "outer", "at"], + *inputs: Any, + **kwargs: Any, + ) -> Any: ... + +@type_check_only +class _UFunc3Kwargs(TypedDict, total=False): + where: _ArrayLikeBool_co | None + casting: _CastingKind + order: _OrderKACF + subok: bool + signature: _3Tuple[str | None] | str | None + +# NOTE: `reduce`, `accumulate`, `reduceat` and `outer` raise a ValueError for +# ufuncs that don't accept two input arguments and return one output argument. +# In such cases the respective methods return `NoReturn` + +# NOTE: Similarly, `at` won't be defined for ufuncs that return +# multiple outputs; in such cases `at` is typed to return `NoReturn` + +# NOTE: If 2 output types are returned then `out` must be a +# 2-tuple of arrays. Otherwise `None` or a plain array are also acceptable + +# pyright: reportIncompatibleMethodOverride=false + +@type_check_only +class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] + @property + def __name__(self) -> _NameType: ... + @property + def __qualname__(self) -> _NameType: ... + @property + def ntypes(self) -> _NTypes: ... + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[1]: ... + @property + def nout(self) -> Literal[1]: ... + @property + def nargs(self) -> Literal[2]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + __x1: _ScalarLike_co, + out: None = ..., + *, + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _2Tuple[None | str] = ..., + ) -> Any: ... + @overload + def __call__( + self, + __x1: ArrayLike, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + *, + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _2Tuple[None | str] = ..., + ) -> NDArray[Any]: ... + @overload + def __call__( + self, + __x1: _SupportsArrayUFunc, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + *, + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _2Tuple[None | str] = ..., + ) -> Any: ... + + def at( + self, + a: _SupportsArrayUFunc, + indices: _ArrayLikeInt_co, + /, + ) -> None: ... + + def reduce(self, *args, **kwargs) -> NoReturn: ... + def accumulate(self, *args, **kwargs) -> NoReturn: ... + def reduceat(self, *args, **kwargs) -> NoReturn: ... + def outer(self, *args, **kwargs) -> NoReturn: ... + +@type_check_only +class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] + @property + def __name__(self) -> _NameType: ... + @property + def __qualname__(self) -> _NameType: ... + @property + def ntypes(self) -> _NTypes: ... + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[2]: ... + @property + def nout(self) -> Literal[1]: ... + @property + def nargs(self) -> Literal[3]: ... + @property + def signature(self) -> None: ... + + @overload # (scalar, scalar) -> scalar + def __call__( + self, + x1: _ScalarLike_co, + x2: _ScalarLike_co, + /, + out: None = None, + *, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> Any: ... + @overload # (array-like, array) -> array + def __call__( + self, + x1: ArrayLike, + x2: NDArray[np.generic], + /, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + *, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array, array-like) -> array + def __call__( + self, + x1: NDArray[np.generic], + x2: ArrayLike, + /, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + *, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array-like, array-like, out=array) -> array + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + /, + out: NDArray[np.generic] | tuple[NDArray[np.generic]], + *, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array-like, array-like) -> array | scalar + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + /, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + *, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any] | Any: ... + + def at( + self, + a: NDArray[Any], + indices: _ArrayLikeInt_co, + b: ArrayLike, + /, + ) -> None: ... + + def reduce( + self, + array: ArrayLike, + axis: None | _ShapeLike = ..., + dtype: DTypeLike = ..., + out: None | NDArray[Any] = ..., + keepdims: bool = ..., + initial: Any = ..., + where: _ArrayLikeBool_co = ..., + ) -> Any: ... + + def accumulate( + self, + array: ArrayLike, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + out: None | NDArray[Any] = ..., + ) -> NDArray[Any]: ... + + def reduceat( + self, + array: ArrayLike, + indices: _ArrayLikeInt_co, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + out: None | NDArray[Any] = ..., + ) -> NDArray[Any]: ... + + @overload # (scalar, scalar) -> scalar + def outer( + self, + A: _ScalarLike_co, + B: _ScalarLike_co, + /, + *, + out: None = None, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> Any: ... + @overload # (array-like, array) -> array + def outer( + self, + A: ArrayLike, + B: NDArray[np.generic], + /, + *, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array, array-like) -> array + def outer( + self, + A: NDArray[np.generic], + B: ArrayLike, + /, + *, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array-like, array-like, out=array) -> array + def outer( + self, + A: ArrayLike, + B: ArrayLike, + /, + *, + out: NDArray[np.generic] | tuple[NDArray[np.generic]], + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any]: ... + @overload # (array-like, array-like) -> array | scalar + def outer( + self, + A: ArrayLike, + B: ArrayLike, + /, + *, + out: NDArray[np.generic] | tuple[NDArray[np.generic]] | None = None, + dtype: DTypeLike | None = None, + **kwds: Unpack[_UFunc3Kwargs], + ) -> NDArray[Any] | Any: ... + +@type_check_only +class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] + @property + def __name__(self) -> _NameType: ... + @property + def __qualname__(self) -> _NameType: ... + @property + def ntypes(self) -> _NTypes: ... + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[1]: ... + @property + def nout(self) -> Literal[2]: ... + @property + def nargs(self) -> Literal[3]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + __x1: _ScalarLike_co, + __out1: None = ..., + __out2: None = ..., + *, + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _3Tuple[None | str] = ..., + ) -> _2Tuple[Any]: ... + @overload + def __call__( + self, + __x1: ArrayLike, + __out1: None | NDArray[Any] = ..., + __out2: None | NDArray[Any] = ..., + *, + out: _2Tuple[NDArray[Any]] = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _3Tuple[None | str] = ..., + ) -> _2Tuple[NDArray[Any]]: ... + @overload + def __call__( + self, + __x1: _SupportsArrayUFunc, + __out1: None | NDArray[Any] = ..., + __out2: None | NDArray[Any] = ..., + *, + out: _2Tuple[NDArray[Any]] = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _3Tuple[None | str] = ..., + ) -> _2Tuple[Any]: ... + + def at(self, *args, **kwargs) -> NoReturn: ... + def reduce(self, *args, **kwargs) -> NoReturn: ... + def accumulate(self, *args, **kwargs) -> NoReturn: ... + def reduceat(self, *args, **kwargs) -> NoReturn: ... + def outer(self, *args, **kwargs) -> NoReturn: ... + +@type_check_only +class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]): # type: ignore[misc] + @property + def __name__(self) -> _NameType: ... + @property + def __qualname__(self) -> _NameType: ... + @property + def ntypes(self) -> _NTypes: ... + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[2]: ... + @property + def nout(self) -> Literal[2]: ... + @property + def nargs(self) -> Literal[4]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + __x1: _ScalarLike_co, + __x2: _ScalarLike_co, + __out1: None = ..., + __out2: None = ..., + *, + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _4Tuple[None | str] = ..., + ) -> _2Tuple[Any]: ... + @overload + def __call__( + self, + __x1: ArrayLike, + __x2: ArrayLike, + __out1: None | NDArray[Any] = ..., + __out2: None | NDArray[Any] = ..., + *, + out: _2Tuple[NDArray[Any]] = ..., + where: None | _ArrayLikeBool_co = ..., + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _4Tuple[None | str] = ..., + ) -> _2Tuple[NDArray[Any]]: ... + + def at(self, *args, **kwargs) -> NoReturn: ... + def reduce(self, *args, **kwargs) -> NoReturn: ... + def accumulate(self, *args, **kwargs) -> NoReturn: ... + def reduceat(self, *args, **kwargs) -> NoReturn: ... + def outer(self, *args, **kwargs) -> NoReturn: ... + +@type_check_only +class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType, _Signature]): # type: ignore[misc] + @property + def __name__(self) -> _NameType: ... + @property + def __qualname__(self) -> _NameType: ... + @property + def ntypes(self) -> _NTypes: ... + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[2]: ... + @property + def nout(self) -> Literal[1]: ... + @property + def nargs(self) -> Literal[3]: ... + @property + def signature(self) -> _Signature: ... + + # Scalar for 1D array-likes; ndarray otherwise + @overload + def __call__( + self, + __x1: ArrayLike, + __x2: ArrayLike, + out: None = ..., + *, + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _3Tuple[None | str] = ..., + axes: list[_2Tuple[SupportsIndex]] = ..., + ) -> Any: ... + @overload + def __call__( + self, + __x1: ArrayLike, + __x2: ArrayLike, + out: NDArray[Any] | tuple[NDArray[Any]], + *, + casting: _CastingKind = ..., + order: _OrderKACF = ..., + dtype: DTypeLike = ..., + subok: bool = ..., + signature: str | _3Tuple[None | str] = ..., + axes: list[_2Tuple[SupportsIndex]] = ..., + ) -> NDArray[Any]: ... + + def at(self, *args, **kwargs) -> NoReturn: ... + def reduce(self, *args, **kwargs) -> NoReturn: ... + def accumulate(self, *args, **kwargs) -> NoReturn: ... + def reduceat(self, *args, **kwargs) -> NoReturn: ... + def outer(self, *args, **kwargs) -> NoReturn: ... + +@type_check_only +class _PyFunc_Kwargs_Nargs2(TypedDict, total=False): + where: None | _ArrayLikeBool_co + casting: _CastingKind + order: _OrderKACF + dtype: DTypeLike + subok: bool + signature: str | tuple[DTypeLike, DTypeLike] + +@type_check_only +class _PyFunc_Kwargs_Nargs3(TypedDict, total=False): + where: None | _ArrayLikeBool_co + casting: _CastingKind + order: _OrderKACF + dtype: DTypeLike + subok: bool + signature: str | tuple[DTypeLike, DTypeLike, DTypeLike] + +@type_check_only +class _PyFunc_Kwargs_Nargs3P(TypedDict, total=False): + where: None | _ArrayLikeBool_co + casting: _CastingKind + order: _OrderKACF + dtype: DTypeLike + subok: bool + signature: str | _3PTuple[DTypeLike] + +@type_check_only +class _PyFunc_Kwargs_Nargs4P(TypedDict, total=False): + where: None | _ArrayLikeBool_co + casting: _CastingKind + order: _OrderKACF + dtype: DTypeLike + subok: bool + signature: str | _4PTuple[DTypeLike] + +@type_check_only +class _PyFunc_Nin1_Nout1(ufunc, Generic[_ReturnType_co, _IDType]): # type: ignore[misc] + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[1]: ... + @property + def nout(self) -> Literal[1]: ... + @property + def nargs(self) -> Literal[2]: ... + @property + def ntypes(self) -> Literal[1]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + x1: _ScalarLike_co, + /, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs2], + ) -> _ReturnType_co: ... + @overload + def __call__( + self, + x1: ArrayLike, + /, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs2], + ) -> _ReturnType_co | NDArray[np.object_]: ... + @overload + def __call__( + self, + x1: ArrayLike, + /, + out: _ArrayType | tuple[_ArrayType], + **kwargs: Unpack[_PyFunc_Kwargs_Nargs2], + ) -> _ArrayType: ... + @overload + def __call__( + self, + x1: _SupportsArrayUFunc, + /, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs2], + ) -> Any: ... + + def at(self, a: _SupportsArrayUFunc, ixs: _ArrayLikeInt_co, /) -> None: ... + def reduce(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def accumulate(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def reduceat(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def outer(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + +@type_check_only +class _PyFunc_Nin2_Nout1(ufunc, Generic[_ReturnType_co, _IDType]): # type: ignore[misc] + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> Literal[2]: ... + @property + def nout(self) -> Literal[1]: ... + @property + def nargs(self) -> Literal[3]: ... + @property + def ntypes(self) -> Literal[1]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + x1: _ScalarLike_co, + x2: _ScalarLike_co, + /, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ReturnType_co: ... + @overload + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + /, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ReturnType_co | NDArray[np.object_]: ... + @overload + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + /, + out: _ArrayType | tuple[_ArrayType], + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ArrayType: ... + @overload + def __call__( + self, + x1: _SupportsArrayUFunc, + x2: _SupportsArrayUFunc | ArrayLike, + /, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> Any: ... + @overload + def __call__( + self, + x1: ArrayLike, + x2: _SupportsArrayUFunc, + /, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> Any: ... + + def at(self, a: _SupportsArrayUFunc, ixs: _ArrayLikeInt_co, b: ArrayLike, /) -> None: ... + + @overload + def reduce( + self, + array: ArrayLike, + axis: None | _ShapeLike, + dtype: DTypeLike, + out: _ArrayType, + /, + keepdims: bool = ..., + initial: _ScalarLike_co = ..., + where: _ArrayLikeBool_co = ..., + ) -> _ArrayType: ... + @overload + def reduce( + self, + /, + array: ArrayLike, + axis: None | _ShapeLike = ..., + dtype: DTypeLike = ..., + *, + out: _ArrayType | tuple[_ArrayType], + keepdims: bool = ..., + initial: _ScalarLike_co = ..., + where: _ArrayLikeBool_co = ..., + ) -> _ArrayType: ... + @overload + def reduce( + self, + /, + array: ArrayLike, + axis: None | _ShapeLike = ..., + dtype: DTypeLike = ..., + out: None = ..., + *, + keepdims: Literal[True], + initial: _ScalarLike_co = ..., + where: _ArrayLikeBool_co = ..., + ) -> NDArray[np.object_]: ... + @overload + def reduce( + self, + /, + array: ArrayLike, + axis: None | _ShapeLike = ..., + dtype: DTypeLike = ..., + out: None = ..., + keepdims: bool = ..., + initial: _ScalarLike_co = ..., + where: _ArrayLikeBool_co = ..., + ) -> _ReturnType_co | NDArray[np.object_]: ... + + @overload + def reduceat( + self, + array: ArrayLike, + indices: _ArrayLikeInt_co, + axis: SupportsIndex, + dtype: DTypeLike, + out: _ArrayType, + /, + ) -> _ArrayType: ... + @overload + def reduceat( + self, + /, + array: ArrayLike, + indices: _ArrayLikeInt_co, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + *, + out: _ArrayType | tuple[_ArrayType], + ) -> _ArrayType: ... + @overload + def reduceat( + self, + /, + array: ArrayLike, + indices: _ArrayLikeInt_co, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + out: None = ..., + ) -> NDArray[np.object_]: ... + @overload + def reduceat( + self, + /, + array: _SupportsArrayUFunc, + indices: _ArrayLikeInt_co, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + ) -> Any: ... + + @overload + def accumulate( + self, + array: ArrayLike, + axis: SupportsIndex, + dtype: DTypeLike, + out: _ArrayType, + /, + ) -> _ArrayType: ... + @overload + def accumulate( + self, + array: ArrayLike, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + *, + out: _ArrayType | tuple[_ArrayType], + ) -> _ArrayType: ... + @overload + def accumulate( + self, + /, + array: ArrayLike, + axis: SupportsIndex = ..., + dtype: DTypeLike = ..., + out: None = ..., + ) -> NDArray[np.object_]: ... + + @overload + def outer( + self, + A: _ScalarLike_co, + B: _ScalarLike_co, + /, *, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ReturnType_co: ... + @overload + def outer( + self, + A: ArrayLike, + B: ArrayLike, + /, *, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ReturnType_co | NDArray[np.object_]: ... + @overload + def outer( + self, + A: ArrayLike, + B: ArrayLike, + /, *, + out: _ArrayType, + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> _ArrayType: ... + @overload + def outer( + self, + A: _SupportsArrayUFunc, + B: _SupportsArrayUFunc | ArrayLike, + /, *, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> Any: ... + @overload + def outer( + self, + A: _ScalarLike_co, + B: _SupportsArrayUFunc | ArrayLike, + /, *, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3], + ) -> Any: ... + +@type_check_only +class _PyFunc_Nin3P_Nout1(ufunc, Generic[_ReturnType_co, _IDType, _NIn]): # type: ignore[misc] + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> _NIn: ... + @property + def nout(self) -> Literal[1]: ... + @property + def ntypes(self) -> Literal[1]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + x1: _ScalarLike_co, + x2: _ScalarLike_co, + x3: _ScalarLike_co, + /, + *xs: _ScalarLike_co, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs4P], + ) -> _ReturnType_co: ... + @overload + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + x3: ArrayLike, + /, + *xs: ArrayLike, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs4P], + ) -> _ReturnType_co | NDArray[np.object_]: ... + @overload + def __call__( + self, + x1: ArrayLike, + x2: ArrayLike, + x3: ArrayLike, + /, + *xs: ArrayLike, + out: _ArrayType | tuple[_ArrayType], + **kwargs: Unpack[_PyFunc_Kwargs_Nargs4P], + ) -> _ArrayType: ... + @overload + def __call__( + self, + x1: _SupportsArrayUFunc | ArrayLike, + x2: _SupportsArrayUFunc | ArrayLike, + x3: _SupportsArrayUFunc | ArrayLike, + /, + *xs: _SupportsArrayUFunc | ArrayLike, + out: None | NDArray[Any] | tuple[NDArray[Any]] = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs4P], + ) -> Any: ... + + def at(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def reduce(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def accumulate(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def reduceat(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def outer(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + +@type_check_only +class _PyFunc_Nin1P_Nout2P(ufunc, Generic[_ReturnType_co, _IDType, _NIn, _NOut]): # type: ignore[misc] + @property + def identity(self) -> _IDType: ... + @property + def nin(self) -> _NIn: ... + @property + def nout(self) -> _NOut: ... + @property + def ntypes(self) -> Literal[1]: ... + @property + def signature(self) -> None: ... + + @overload + def __call__( + self, + x1: _ScalarLike_co, + /, + *xs: _ScalarLike_co, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3P], + ) -> _2PTuple[_ReturnType_co]: ... + @overload + def __call__( + self, + x1: ArrayLike, + /, + *xs: ArrayLike, + out: None = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3P], + ) -> _2PTuple[_ReturnType_co | NDArray[np.object_]]: ... + @overload + def __call__( + self, + x1: ArrayLike, + /, + *xs: ArrayLike, + out: _2PTuple[_ArrayType], + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3P], + ) -> _2PTuple[_ArrayType]: ... + @overload + def __call__( + self, + x1: _SupportsArrayUFunc | ArrayLike, + /, + *xs: _SupportsArrayUFunc | ArrayLike, + out: None | _2PTuple[NDArray[Any]] = ..., + **kwargs: Unpack[_PyFunc_Kwargs_Nargs3P], + ) -> Any: ... + + def at(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def reduce(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def accumulate(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def reduceat(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... + def outer(self, /, *args: Any, **kwargs: Any) -> NoReturn: ... diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.py b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cc1a36c437be2053364bbdda40bb932a013fe01d --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.py @@ -0,0 +1,88 @@ +""" +This is a module for defining private helpers which do not depend on the +rest of NumPy. + +Everything in here must be self-contained so that it can be +imported anywhere else without creating circular imports. +If a utility requires the import of NumPy, it probably belongs +in ``numpy._core``. +""" + +import functools +import warnings +from ._convertions import asunicode, asbytes + + +def set_module(module): + """Private decorator for overriding __module__ on a function or class. + + Example usage:: + + @set_module('numpy') + def example(): + pass + + assert example.__module__ == 'numpy' + """ + def decorator(func): + if module is not None: + func.__module__ = module + return func + return decorator + + +def _rename_parameter(old_names, new_names, dep_version=None): + """ + Generate decorator for backward-compatible keyword renaming. + + Apply the decorator generated by `_rename_parameter` to functions with a + renamed parameter to maintain backward-compatibility. + + After decoration, the function behaves as follows: + If only the new parameter is passed into the function, behave as usual. + If only the old parameter is passed into the function (as a keyword), raise + a DeprecationWarning if `dep_version` is provided, and behave as usual + otherwise. + If both old and new parameters are passed into the function, raise a + DeprecationWarning if `dep_version` is provided, and raise the appropriate + TypeError (function got multiple values for argument). + + Parameters + ---------- + old_names : list of str + Old names of parameters + new_name : list of str + New names of parameters + dep_version : str, optional + Version of NumPy in which old parameter was deprecated in the format + 'X.Y.Z'. If supplied, the deprecation message will indicate that + support for the old parameter will be removed in version 'X.Y+2.Z' + + Notes + ----- + Untested with functions that accept *args. Probably won't work as written. + + """ + def decorator(fun): + @functools.wraps(fun) + def wrapper(*args, **kwargs): + __tracebackhide__ = True # Hide traceback for py.test + for old_name, new_name in zip(old_names, new_names): + if old_name in kwargs: + if dep_version: + end_version = dep_version.split('.') + end_version[1] = str(int(end_version[1]) + 2) + end_version = '.'.join(end_version) + msg = (f"Use of keyword argument `{old_name}` is " + f"deprecated and replaced by `{new_name}`. " + f"Support for `{old_name}` will be removed " + f"in NumPy {end_version}.") + warnings.warn(msg, DeprecationWarning, stacklevel=2) + if new_name in kwargs: + msg = (f"{fun.__name__}() got multiple values for " + f"argument now known as `{new_name}`") + raise TypeError(msg) + kwargs[new_name] = kwargs.pop(old_name) + return fun(*args, **kwargs) + return wrapper + return decorator diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..de68d8d2800e71b6483d6caee740581d8e08a977 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__init__.pyi @@ -0,0 +1,31 @@ +from collections.abc import Callable, Iterable +from typing import Protocol, overload, type_check_only + +from _typeshed import IdentityFunction +from typing_extensions import TypeVar + +from ._convertions import asbytes as asbytes +from ._convertions import asunicode as asunicode + +### + +_T = TypeVar("_T") +_HasModuleT = TypeVar("_HasModuleT", bound=_HasModule) + +@type_check_only +class _HasModule(Protocol): + __module__: str + +### + +@overload +def set_module(module: None) -> IdentityFunction: ... +@overload +def set_module(module: _HasModuleT) -> _HasModuleT: ... + +# +def _rename_parameter( + old_names: Iterable[str], + new_names: Iterable[str], + dep_version: str | None = None, +) -> Callable[[Callable[..., _T]], Callable[..., _T]]: ... diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5033180fe4f02e738daaaaa2d34c06392b354596 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c7ab2e9c1c0dad6bf3245ac73fd2f1474cb7d7ec Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..63361e9d35d0bf0a21bd3fbe8d7d7ea8b26f2d31 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3d0712873ff30bcc687d744d667dd26ab4ed5fb7 Binary files /dev/null and b/pythonProject/.venv/Lib/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc differ diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.py b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.py new file mode 100644 index 0000000000000000000000000000000000000000..b6c0360c1ce749ef79b103bc32cb1cd78f0745b0 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.py @@ -0,0 +1,18 @@ +""" +A set of methods retained from np.compat module that +are still used across codebase. +""" + +__all__ = ["asunicode", "asbytes"] + + +def asunicode(s): + if isinstance(s, bytes): + return s.decode('latin1') + return str(s) + + +def asbytes(s): + if isinstance(s, bytes): + return s + return str(s).encode('latin1') diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..63167182aa781f549f5f4b67b67e615c43e02c62 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_convertions.pyi @@ -0,0 +1,4 @@ +__all__ = ["asbytes", "asunicode"] + +def asunicode(s: bytes | str) -> str: ... +def asbytes(s: bytes | str) -> str: ... diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.py b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.py new file mode 100644 index 0000000000000000000000000000000000000000..381ca3940b8aeb41ad1ad34154a1b19beb56dc27 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.py @@ -0,0 +1,191 @@ +"""Subset of inspect module from upstream python + +We use this instead of upstream because upstream inspect is slow to import, and +significantly contributes to numpy import times. Importing this copy has almost +no overhead. + +""" +import types + +__all__ = ['getargspec', 'formatargspec'] + +# ----------------------------------------------------------- type-checking +def ismethod(object): + """Return true if the object is an instance method. + + Instance method objects provide these attributes: + __doc__ documentation string + __name__ name with which this method was defined + im_class class object in which this method belongs + im_func function object containing implementation of method + im_self instance to which this method is bound, or None + + """ + return isinstance(object, types.MethodType) + +def isfunction(object): + """Return true if the object is a user-defined function. + + Function objects provide these attributes: + __doc__ documentation string + __name__ name with which this function was defined + func_code code object containing compiled function bytecode + func_defaults tuple of any default values for arguments + func_doc (same as __doc__) + func_globals global namespace in which this function was defined + func_name (same as __name__) + + """ + return isinstance(object, types.FunctionType) + +def iscode(object): + """Return true if the object is a code object. + + Code objects provide these attributes: + co_argcount number of arguments (not including * or ** args) + co_code string of raw compiled bytecode + co_consts tuple of constants used in the bytecode + co_filename name of file in which this code object was created + co_firstlineno number of first line in Python source code + co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg + co_lnotab encoded mapping of line numbers to bytecode indices + co_name name with which this code object was defined + co_names tuple of names of local variables + co_nlocals number of local variables + co_stacksize virtual machine stack space required + co_varnames tuple of names of arguments and local variables + + """ + return isinstance(object, types.CodeType) + +# ------------------------------------------------ argument list extraction +# These constants are from Python's compile.h. +CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8 + +def getargs(co): + """Get information about the arguments accepted by a code object. + + Three things are returned: (args, varargs, varkw), where 'args' is + a list of argument names (possibly containing nested lists), and + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + + """ + + if not iscode(co): + raise TypeError('arg is not a code object') + + nargs = co.co_argcount + names = co.co_varnames + args = list(names[:nargs]) + + # The following acrobatics are for anonymous (tuple) arguments. + # Which we do not need to support, so remove to avoid importing + # the dis module. + for i in range(nargs): + if args[i][:1] in ['', '.']: + raise TypeError("tuple function arguments are not supported") + varargs = None + if co.co_flags & CO_VARARGS: + varargs = co.co_varnames[nargs] + nargs = nargs + 1 + varkw = None + if co.co_flags & CO_VARKEYWORDS: + varkw = co.co_varnames[nargs] + return args, varargs, varkw + +def getargspec(func): + """Get the names and default values of a function's arguments. + + A tuple of four things is returned: (args, varargs, varkw, defaults). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'defaults' is an n-tuple of the default values of the last n arguments. + + """ + + if ismethod(func): + func = func.__func__ + if not isfunction(func): + raise TypeError('arg is not a Python function') + args, varargs, varkw = getargs(func.__code__) + return args, varargs, varkw, func.__defaults__ + +def getargvalues(frame): + """Get information about arguments passed into a particular frame. + + A tuple of four things is returned: (args, varargs, varkw, locals). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'locals' is the locals dictionary of the given frame. + + """ + args, varargs, varkw = getargs(frame.f_code) + return args, varargs, varkw, frame.f_locals + +def joinseq(seq): + if len(seq) == 1: + return '(' + seq[0] + ',)' + else: + return '(' + ', '.join(seq) + ')' + +def strseq(object, convert, join=joinseq): + """Recursively walk a sequence, stringifying each element. + + """ + if type(object) in [list, tuple]: + return join([strseq(_o, convert, join) for _o in object]) + else: + return convert(object) + +def formatargspec(args, varargs=None, varkw=None, defaults=None, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargspec. + + The first four arguments are (args, varargs, varkw, defaults). The + other four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + specs = [] + if defaults: + firstdefault = len(args) - len(defaults) + for i in range(len(args)): + spec = strseq(args[i], formatarg, join) + if defaults and i >= firstdefault: + spec = spec + formatvalue(defaults[i - firstdefault]) + specs.append(spec) + if varargs is not None: + specs.append(formatvarargs(varargs)) + if varkw is not None: + specs.append(formatvarkw(varkw)) + return '(' + ', '.join(specs) + ')' + +def formatargvalues(args, varargs, varkw, locals, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargvalues. + + The first four arguments are (args, varargs, varkw, locals). The + next four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + def convert(name, locals=locals, + formatarg=formatarg, formatvalue=formatvalue): + return formatarg(name) + formatvalue(locals[name]) + specs = [strseq(arg, convert, join) for arg in args] + + if varargs: + specs.append(formatvarargs(varargs) + formatvalue(locals[varargs])) + if varkw: + specs.append(formatvarkw(varkw) + formatvalue(locals[varkw])) + return '(' + ', '.join(specs) + ')' diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8eba974f839b44e0d772695f0e503c415306dc75 --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_inspect.pyi @@ -0,0 +1,71 @@ +import types +from collections.abc import Callable, Mapping +from typing import Any, Final, TypeAlias, overload + +from _typeshed import SupportsLenAndGetItem +from typing_extensions import TypeIs, TypeVar + +__all__ = ["formatargspec", "getargspec"] + +### + +_T = TypeVar("_T") +_RT = TypeVar("_RT") + +_StrSeq: TypeAlias = SupportsLenAndGetItem[str] +_NestedSeq: TypeAlias = list[_T | _NestedSeq[_T]] | tuple[_T | _NestedSeq[_T], ...] + +_JoinFunc: TypeAlias = Callable[[list[_T]], _T] +_FormatFunc: TypeAlias = Callable[[_T], str] + +### + +CO_OPTIMIZED: Final = 1 +CO_NEWLOCALS: Final = 2 +CO_VARARGS: Final = 4 +CO_VARKEYWORDS: Final = 8 + +### + +def ismethod(object: object) -> TypeIs[types.MethodType]: ... +def isfunction(object: object) -> TypeIs[types.FunctionType]: ... +def iscode(object: object) -> TypeIs[types.CodeType]: ... + +### + +def getargs(co: types.CodeType) -> tuple[list[str], str | None, str | None]: ... +def getargspec(func: types.MethodType | types.FunctionType) -> tuple[list[str], str | None, str | None, tuple[Any, ...]]: ... +def getargvalues(frame: types.FrameType) -> tuple[list[str], str | None, str | None, dict[str, Any]]: ... + +# +def joinseq(seq: _StrSeq) -> str: ... + +# +@overload +def strseq(object: _NestedSeq[str], convert: Callable[[Any], Any], join: _JoinFunc[str] = ...) -> str: ... +@overload +def strseq(object: _NestedSeq[_T], convert: Callable[[_T], _RT], join: _JoinFunc[_RT]) -> _RT: ... + +# +def formatargspec( + args: _StrSeq, + varargs: str | None = None, + varkw: str | None = None, + defaults: SupportsLenAndGetItem[object] | None = None, + formatarg: _FormatFunc[str] = ..., # str + formatvarargs: _FormatFunc[str] = ..., # "*{}".format + formatvarkw: _FormatFunc[str] = ..., # "**{}".format + formatvalue: _FormatFunc[object] = ..., # "={!r}".format + join: _JoinFunc[str] = ..., # joinseq +) -> str: ... +def formatargvalues( + args: _StrSeq, + varargs: str | None, + varkw: str | None, + locals: Mapping[str, object] | None, + formatarg: _FormatFunc[str] = ..., # str + formatvarargs: _FormatFunc[str] = ..., # "*{}".format + formatvarkw: _FormatFunc[str] = ..., # "**{}".format + formatvalue: _FormatFunc[object] = ..., # "={!r}".format + join: _JoinFunc[str] = ..., # joinseq +) -> str: ... diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.py b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.py new file mode 100644 index 0000000000000000000000000000000000000000..6f56d60c86179b3b669acd92c307cd95991504ac --- /dev/null +++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.py @@ -0,0 +1,487 @@ +"""Utility to compare pep440 compatible version strings. + +The LooseVersion and StrictVersion classes that distutils provides don't +work; they don't recognize anything like alpha/beta/rc/dev versions. +""" + +# Copyright (c) Donald Stufft and individual contributors. +# 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. + +# 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. + +import collections +import itertools +import re + + +__all__ = [ + "parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN", +] + + +# BEGIN packaging/_structures.py + + +class Infinity: + def __repr__(self): + return "Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return False + + def __le__(self, other): + return False + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return True + + def __ge__(self, other): + return True + + def __neg__(self): + return NegativeInfinity + + +Infinity = Infinity() + + +class NegativeInfinity: + def __repr__(self): + return "-Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return True + + def __le__(self, other): + return True + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return False + + def __ge__(self, other): + return False + + def __neg__(self): + return Infinity + + +# BEGIN packaging/version.py + + +NegativeInfinity = NegativeInfinity() + +_Version = collections.namedtuple( + "_Version", + ["epoch", "release", "dev", "pre", "post", "local"], +) + + +def parse(version): + """ + Parse the given version string and return either a :class:`Version` object + or a :class:`LegacyVersion` object depending on if the given version is + a valid PEP 440 version or a legacy version. + """ + try: + return Version(version) + except InvalidVersion: + return LegacyVersion(version) + + +class InvalidVersion(ValueError): + """ + An invalid version was found, users should refer to PEP 440. + """ + + +class _BaseVersion: + + def __hash__(self): + return hash(self._key) + + def __lt__(self, other): + return self._compare(other, lambda s, o: s < o) + + def __le__(self, other): + return self._compare(other, lambda s, o: s <= o) + + def __eq__(self, other): + return self._compare(other, lambda s, o: s == o) + + def __ge__(self, other): + return self._compare(other, lambda s, o: s >= o) + + def __gt__(self, other): + return self._compare(other, lambda s, o: s > o) + + def __ne__(self, other): + return self._compare(other, lambda s, o: s != o) + + def _compare(self, other, method): + if not isinstance(other, _BaseVersion): + return NotImplemented + + return method(self._key, other._key) + + +class LegacyVersion(_BaseVersion): + + def __init__(self, version): + self._version = str(version) + self._key = _legacy_cmpkey(self._version) + + def __str__(self): + return self._version + + def __repr__(self): + return "".format(repr(str(self))) + + @property + def public(self): + return self._version + + @property + def base_version(self): + return self._version + + @property + def local(self): + return None + + @property + def is_prerelease(self): + return False + + @property + def is_postrelease(self): + return False + + +_legacy_version_component_re = re.compile( + r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE, +) + +_legacy_version_replacement_map = { + "pre": "c", "preview": "c", "-": "final-", "rc": "c", "dev": "@", +} + + +def _parse_version_parts(s): + for part in _legacy_version_component_re.split(s): + part = _legacy_version_replacement_map.get(part, part) + + if not part or part == ".": + continue + + if part[:1] in "0123456789": + # pad for numeric comparison + yield part.zfill(8) + else: + yield "*" + part + + # ensure that alpha/beta/candidate are before final + yield "*final" + + +def _legacy_cmpkey(version): + # We hardcode an epoch of -1 here. A PEP 440 version can only have an epoch + # greater than or equal to 0. This will effectively put the LegacyVersion, + # which uses the defacto standard originally implemented by setuptools, + # as before all PEP 440 versions. + epoch = -1 + + # This scheme is taken from pkg_resources.parse_version setuptools prior to + # its adoption of the packaging library. + parts = [] + for part in _parse_version_parts(version.lower()): + if part.startswith("*"): + # remove "-" before a prerelease tag + if part < "*final": + while parts and parts[-1] == "*final-": + parts.pop() + + # remove trailing zeros from each series of numeric parts + while parts and parts[-1] == "00000000": + parts.pop() + + parts.append(part) + parts = tuple(parts) + + return epoch, parts + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(
+        r"^\s*" + VERSION_PATTERN + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    def __init__(self, version):
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion("Invalid version: '{0}'".format(version))
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(
+                match.group("pre_l"),
+                match.group("pre_n"),
+            ),
+            post=_parse_letter_version(
+                match.group("post_l"),
+                match.group("post_n1") or match.group("post_n2"),
+            ),
+            dev=_parse_letter_version(
+                match.group("dev_l"),
+                match.group("dev_n"),
+            ),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self):
+        return "".format(repr(str(self)))
+
+    def __str__(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        # Pre-release
+        if self._version.pre is not None:
+            parts.append("".join(str(x) for x in self._version.pre))
+
+        # Post-release
+        if self._version.post is not None:
+            parts.append(".post{0}".format(self._version.post[1]))
+
+        # Development release
+        if self._version.dev is not None:
+            parts.append(".dev{0}".format(self._version.dev[1]))
+
+        # Local version segment
+        if self._version.local is not None:
+            parts.append(
+                "+{0}".format(".".join(str(x) for x in self._version.local))
+            )
+
+        return "".join(parts)
+
+    @property
+    def public(self):
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        return "".join(parts)
+
+    @property
+    def local(self):
+        version_string = str(self)
+        if "+" in version_string:
+            return version_string.split("+", 1)[1]
+
+    @property
+    def is_prerelease(self):
+        return bool(self._version.dev or self._version.pre)
+
+    @property
+    def is_postrelease(self):
+        return bool(self._version.post)
+
+
+def _parse_letter_version(letter, number):
+    if letter:
+        # We assume there is an implicit 0 in a pre-release if there is
+        # no numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower-case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume that if we are given a number but not given a letter,
+        # then this is using the implicit post release syntax (e.g., 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+
+_local_version_seperators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local):
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_seperators.split(local)
+        )
+
+
+def _cmpkey(epoch, release, pre, post, dev, local):
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non-zero, then take the rest,
+    # re-reverse it back into the correct order, and make it a tuple and use
+    # that for our sorting key.
+    release = tuple(
+        reversed(list(
+            itertools.dropwhile(
+                lambda x: x == 0,
+                reversed(release),
+            )
+        ))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre-segment, but we _only_ want to do this
+    # if there is no pre- or a post-segment. If we have one of those, then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        pre = -Infinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        pre = Infinity
+
+    # Versions without a post-segment should sort before those with one.
+    if post is None:
+        post = -Infinity
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        dev = Infinity
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        local = -Infinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alphanumeric segments sort before numeric segments
+        # - Alphanumeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        local = tuple(
+            (i, "") if isinstance(i, int) else (-Infinity, i)
+            for i in local
+        )
+
+    return epoch, release, pre, post, dev, local
diff --git a/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.pyi b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..42c46405dd3717a0c3ef04fa98c88786b93e379f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/numpy/_utils/_pep440.pyi
@@ -0,0 +1,121 @@
+import re
+from collections.abc import Callable
+from typing import (
+    Any,
+    ClassVar,
+    Final,
+    Generic,
+    NamedTuple,
+    TypeVar,
+    final,
+    type_check_only,
+)
+from typing import (
+    Literal as L,
+)
+
+from typing_extensions import TypeIs
+
+__all__ = ["VERSION_PATTERN", "InvalidVersion", "LegacyVersion", "Version", "parse"]
+
+###
+
+_CmpKeyT = TypeVar("_CmpKeyT", bound=tuple[object, ...])
+_CmpKeyT_co = TypeVar("_CmpKeyT_co", bound=tuple[object, ...], default=tuple[Any, ...], covariant=True)
+
+###
+
+VERSION_PATTERN: Final[str] = ...
+
+class InvalidVersion(ValueError): ...
+
+@type_check_only
+@final
+class _InfinityType:
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: object, /) -> TypeIs[_InfinityType]: ...
+    def __ne__(self, other: object, /) -> bool: ...
+    def __lt__(self, other: object, /) -> L[False]: ...
+    def __le__(self, other: object, /) -> L[False]: ...
+    def __gt__(self, other: object, /) -> L[True]: ...
+    def __ge__(self, other: object, /) -> L[True]: ...
+    def __neg__(self) -> _NegativeInfinityType: ...
+
+Infinity: Final[_InfinityType] = ...
+
+@type_check_only
+@final
+class _NegativeInfinityType:
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: object, /) -> TypeIs[_NegativeInfinityType]: ...
+    def __ne__(self, other: object, /) -> bool: ...
+    def __lt__(self, other: object, /) -> L[True]: ...
+    def __le__(self, other: object, /) -> L[True]: ...
+    def __gt__(self, other: object, /) -> L[False]: ...
+    def __ge__(self, other: object, /) -> L[False]: ...
+    def __neg__(self) -> _InfinityType: ...
+
+NegativeInfinity: Final[_NegativeInfinityType] = ...
+
+class _Version(NamedTuple):
+    epoch: int
+    release: tuple[int, ...]
+    dev: tuple[str, int] | None
+    pre: tuple[str, int] | None
+    post: tuple[str, int] | None
+    local: tuple[str | int, ...] | None
+
+class _BaseVersion(Generic[_CmpKeyT_co]):
+    _key: _CmpKeyT_co
+    def __hash__(self) -> int: ...
+    def __eq__(self, other: _BaseVersion, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __ne__(self, other: _BaseVersion, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __lt__(self, other: _BaseVersion, /) -> bool: ...
+    def __le__(self, other: _BaseVersion, /) -> bool: ...
+    def __ge__(self, other: _BaseVersion, /) -> bool: ...
+    def __gt__(self, other: _BaseVersion, /) -> bool: ...
+    def _compare(self, /, other: _BaseVersion[_CmpKeyT], method: Callable[[_CmpKeyT_co, _CmpKeyT], bool]) -> bool: ...
+
+class LegacyVersion(_BaseVersion[tuple[L[-1], tuple[str, ...]]]):
+    _version: Final[str]
+    def __init__(self, /, version: str) -> None: ...
+    @property
+    def public(self) -> str: ...
+    @property
+    def base_version(self) -> str: ...
+    @property
+    def local(self) -> None: ...
+    @property
+    def is_prerelease(self) -> L[False]: ...
+    @property
+    def is_postrelease(self) -> L[False]: ...
+
+class Version(
+    _BaseVersion[
+        tuple[
+            int,  # epoch
+            tuple[int, ...],  # release
+            tuple[str, int] | _InfinityType | _NegativeInfinityType,  # pre
+            tuple[str, int] | _NegativeInfinityType,  # post
+            tuple[str, int] | _InfinityType,  # dev
+            tuple[tuple[int, L[""]] | tuple[_NegativeInfinityType, str], ...] | _NegativeInfinityType,  # local
+        ],
+    ],
+):
+    _regex: ClassVar[re.Pattern[str]] = ...
+    _version: Final[str]
+
+    def __init__(self, /, version: str) -> None: ...
+    @property
+    def public(self) -> str: ...
+    @property
+    def base_version(self) -> str: ...
+    @property
+    def local(self) -> str | None: ...
+    @property
+    def is_prerelease(self) -> bool: ...
+    @property
+    def is_postrelease(self) -> bool: ...
+
+#
+def parse(version: str) -> Version | LegacyVersion: ...
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8a28e576e0e18d9742347dfe5a63247d230ba50
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/__init__.py
@@ -0,0 +1,370 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+__all__ = [
+    # Constants
+    "ONNX_ML",
+    "IR_VERSION",
+    "IR_VERSION_2017_10_10",
+    "IR_VERSION_2017_10_30",
+    "IR_VERSION_2017_11_3",
+    "IR_VERSION_2019_1_22",
+    "IR_VERSION_2019_3_18",
+    "IR_VERSION_2019_9_19",
+    "IR_VERSION_2020_5_8",
+    "IR_VERSION_2021_7_30",
+    "IR_VERSION_2023_5_5",
+    "IR_VERSION_2024_3_25",
+    "EXPERIMENTAL",
+    "STABLE",
+    # Modules
+    "checker",
+    "compose",
+    "defs",
+    "gen_proto",
+    "helper",
+    "hub",
+    "numpy_helper",
+    "parser",
+    "printer",
+    "shape_inference",
+    "utils",
+    "version_converter",
+    # Proto classes
+    "AttributeProto",
+    "DeviceConfigurationProto",
+    "FunctionProto",
+    "GraphProto",
+    "IntIntListEntryProto",
+    "MapProto",
+    "ModelProto",
+    "NodeDeviceConfigurationProto",
+    "NodeProto",
+    "OperatorProto",
+    "OperatorSetIdProto",
+    "OperatorSetProto",
+    "OperatorStatus",
+    "OptionalProto",
+    "SequenceProto",
+    "SimpleShardedDimProto",
+    "ShardedDimProto",
+    "ShardingSpecProto",
+    "SparseTensorProto",
+    "StringStringEntryProto",
+    "TensorAnnotation",
+    "TensorProto",
+    "TensorShapeProto",
+    "TrainingInfoProto",
+    "TypeProto",
+    "ValueInfoProto",
+    "Version",
+    # Utility functions
+    "convert_model_to_external_data",
+    "load_external_data_for_model",
+    "load_model_from_string",
+    "load_model",
+    "load_tensor_from_string",
+    "load_tensor",
+    "save_model",
+    "save_tensor",
+    "write_external_data_tensors",
+]
+# isort:skip_file
+
+import os
+import typing
+from typing import IO, Literal, Union
+
+
+from onnx import serialization
+from onnx.onnx_cpp2py_export import ONNX_ML
+from onnx.external_data_helper import (
+    load_external_data_for_model,
+    write_external_data_tensors,
+    convert_model_to_external_data,
+)
+from onnx.onnx_pb import (
+    AttributeProto,
+    DeviceConfigurationProto,
+    EXPERIMENTAL,
+    FunctionProto,
+    GraphProto,
+    IntIntListEntryProto,
+    IR_VERSION,
+    IR_VERSION_2017_10_10,
+    IR_VERSION_2017_10_30,
+    IR_VERSION_2017_11_3,
+    IR_VERSION_2019_1_22,
+    IR_VERSION_2019_3_18,
+    IR_VERSION_2019_9_19,
+    IR_VERSION_2020_5_8,
+    IR_VERSION_2021_7_30,
+    IR_VERSION_2023_5_5,
+    IR_VERSION_2024_3_25,
+    ModelProto,
+    NodeDeviceConfigurationProto,
+    NodeProto,
+    OperatorSetIdProto,
+    OperatorStatus,
+    STABLE,
+    SimpleShardedDimProto,
+    ShardedDimProto,
+    ShardingSpecProto,
+    SparseTensorProto,
+    StringStringEntryProto,
+    TensorAnnotation,
+    TensorProto,
+    TensorShapeProto,
+    TrainingInfoProto,
+    TypeProto,
+    ValueInfoProto,
+    Version,
+)
+from onnx.onnx_operators_pb import OperatorProto, OperatorSetProto
+from onnx.onnx_data_pb import MapProto, OptionalProto, SequenceProto
+import onnx.version
+
+# Import common subpackages so they're available when you 'import onnx'
+from onnx import (
+    checker,
+    compose,
+    defs,
+    gen_proto,
+    helper,
+    hub,
+    numpy_helper,
+    parser,
+    printer,
+    shape_inference,
+    utils,
+    version_converter,
+)
+
+__version__ = onnx.version.version
+
+# Supported model formats that can be loaded from and saved to
+# The literals are formats with built-in support. But we also allow users to
+# register their own formats. So we allow str as well.
+_SupportedFormat = Union[
+    Literal["protobuf", "textproto", "onnxtxt", "json"], str  # noqa: PYI051
+]
+# Default serialization format
+_DEFAULT_FORMAT = "protobuf"
+
+
+def _load_bytes(f: IO[bytes] | str | os.PathLike) -> bytes:
+    if hasattr(f, "read") and callable(typing.cast("IO[bytes]", f).read):
+        content = typing.cast("IO[bytes]", f).read()
+    else:
+        f = typing.cast("Union[str, os.PathLike]", f)
+        with open(f, "rb") as readable:
+            content = readable.read()
+    return content
+
+
+def _save_bytes(content: bytes, f: IO[bytes] | str | os.PathLike) -> None:
+    if hasattr(f, "write") and callable(typing.cast("IO[bytes]", f).write):
+        typing.cast("IO[bytes]", f).write(content)
+    else:
+        f = typing.cast("Union[str, os.PathLike]", f)
+        with open(f, "wb") as writable:
+            writable.write(content)
+
+
+def _get_file_path(f: IO[bytes] | str | os.PathLike | None) -> str | None:
+    if isinstance(f, (str, os.PathLike)):
+        return os.path.abspath(f)
+    if hasattr(f, "name"):
+        assert f is not None
+        return os.path.abspath(f.name)
+    return None
+
+
+def _get_serializer(
+    fmt: _SupportedFormat | None, f: str | os.PathLike | IO[bytes] | None = None
+) -> serialization.ProtoSerializer:
+    """Get the serializer for the given path and format from the serialization registry."""
+    # Use fmt if it is specified
+    if fmt is not None:
+        return serialization.registry.get(fmt)
+
+    if (file_path := _get_file_path(f)) is not None:
+        _, ext = os.path.splitext(file_path)
+        fmt = serialization.registry.get_format_from_file_extension(ext)
+
+    # Failed to resolve format if fmt is None. Use protobuf as default
+    fmt = fmt or _DEFAULT_FORMAT
+    assert fmt is not None
+
+    return serialization.registry.get(fmt)
+
+
+def load_model(
+    f: IO[bytes] | str | os.PathLike,
+    format: _SupportedFormat | None = None,  # noqa: A002
+    load_external_data: bool = True,
+) -> ModelProto:
+    """Loads a serialized ModelProto into memory.
+
+    Args:
+        f: can be a file-like object (has "read" function) or a string/PathLike containing a file name
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+        load_external_data: Whether to load the external data.
+            Set to True if the data is under the same directory of the model.
+            If not, users need to call :func:`load_external_data_for_model`
+            with directory to load external data from.
+
+    Returns:
+        Loaded in-memory ModelProto.
+    """
+    model = _get_serializer(format, f).deserialize_proto(_load_bytes(f), ModelProto())
+
+    if load_external_data:
+        model_filepath = _get_file_path(f)
+        if model_filepath:
+            base_dir = os.path.dirname(model_filepath)
+            load_external_data_for_model(model, base_dir)
+
+    return model
+
+
+def load_tensor(
+    f: IO[bytes] | str | os.PathLike,
+    format: _SupportedFormat | None = None,  # noqa: A002
+) -> TensorProto:
+    """Loads a serialized TensorProto into memory.
+
+    Args:
+        f: can be a file-like object (has "read" function) or a string/PathLike containing a file name
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+
+    Returns:
+        Loaded in-memory TensorProto.
+    """
+    return _get_serializer(format, f).deserialize_proto(_load_bytes(f), TensorProto())
+
+
+def load_model_from_string(
+    s: bytes | str,
+    format: _SupportedFormat = _DEFAULT_FORMAT,  # noqa: A002
+) -> ModelProto:
+    """Loads a binary string (bytes) that contains serialized ModelProto.
+
+    Args:
+        s: a string, which contains serialized ModelProto
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+
+    Returns:
+        Loaded in-memory ModelProto.
+    """
+    return _get_serializer(format).deserialize_proto(s, ModelProto())
+
+
+def load_tensor_from_string(
+    s: bytes,
+    format: _SupportedFormat = _DEFAULT_FORMAT,  # noqa: A002
+) -> TensorProto:
+    """Loads a binary string (bytes) that contains serialized TensorProto.
+
+    Args:
+        s: a string, which contains serialized TensorProto
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+
+    Returns:
+        Loaded in-memory TensorProto.
+    """
+    return _get_serializer(format).deserialize_proto(s, TensorProto())
+
+
+def save_model(
+    proto: ModelProto | bytes,
+    f: IO[bytes] | str | os.PathLike,
+    format: _SupportedFormat | None = None,  # noqa: A002
+    *,
+    save_as_external_data: bool = False,
+    all_tensors_to_one_file: bool = True,
+    location: str | None = None,
+    size_threshold: int = 1024,
+    convert_attribute: bool = False,
+) -> None:
+    """Saves the ModelProto to the specified path and optionally, serialize tensors with raw data as external data before saving.
+
+    Args:
+        proto: should be a in-memory ModelProto
+        f: can be a file-like object (has "write" function) or a string containing
+        a file name or a pathlike object
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+        save_as_external_data: If true, save tensors to external file(s).
+        all_tensors_to_one_file: Effective only if save_as_external_data is True.
+            If true, save all tensors to one external file specified by location.
+            If false, save each tensor to a file named with the tensor name.
+        location: Effective only if save_as_external_data is true.
+            Specify the external file that all tensors to save to.
+            Path is relative to the model path.
+            If not specified, will use the model name.
+        size_threshold: Effective only if save_as_external_data is True.
+            Threshold for size of data. Only when tensor's data is >= the size_threshold it will be converted
+            to external data. To convert every tensor with raw data to external data set size_threshold=0.
+        convert_attribute: Effective only if save_as_external_data is True.
+            If true, convert all tensors to external data
+            If false, convert only non-attribute tensors to external data
+    """
+    if isinstance(proto, bytes):
+        proto = _get_serializer(_DEFAULT_FORMAT).deserialize_proto(proto, ModelProto())
+
+    if save_as_external_data:
+        convert_model_to_external_data(
+            proto, all_tensors_to_one_file, location, size_threshold, convert_attribute
+        )
+
+    model_filepath = _get_file_path(f)
+    if model_filepath is not None:
+        basepath = os.path.dirname(model_filepath)
+        proto = write_external_data_tensors(proto, basepath)
+
+    serialized = _get_serializer(format, model_filepath).serialize_proto(proto)
+    _save_bytes(serialized, f)
+
+
+def save_tensor(
+    proto: TensorProto,
+    f: IO[bytes] | str | os.PathLike,
+    format: _SupportedFormat | None = None,  # noqa: A002
+) -> None:
+    """Saves the TensorProto to the specified path.
+
+    Args:
+        proto: should be a in-memory TensorProto
+        f: can be a file-like object (has "write" function) or a string
+        containing a file name or a pathlike object.
+        format: The serialization format. When it is not specified, it is inferred
+            from the file extension when ``f`` is a path. If not specified _and_
+            ``f`` is not a path, 'protobuf' is used. The encoding is assumed to
+            be "utf-8" when the format is a text format.
+    """
+    serialized = _get_serializer(format, f).serialize_proto(proto)
+    _save_bytes(serialized, f)
+
+
+# For backward compatibility
+load = load_model
+load_from_string = load_model_from_string
+save = save_model
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/_mapping.py b/pythonProject/.venv/Lib/site-packages/onnx/_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..110fc571504df0d246a2cb406bed06978c76aba6
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/_mapping.py
@@ -0,0 +1,113 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import NamedTuple
+
+import ml_dtypes
+import numpy as np
+
+from onnx.onnx_pb import TensorProto
+
+
+class TensorDtypeMap(NamedTuple):
+    np_dtype: np.dtype
+    storage_dtype: int
+    name: str
+
+
+# tensor_dtype: (numpy type, storage type, string name)
+# The storage type is the type used to store the tensor in the *_data field of
+# a TensorProto. All available fields are float_data, int32_data, int64_data,
+# string_data, uint64_data and double_data.
+TENSOR_TYPE_MAP: dict[int, TensorDtypeMap] = {
+    int(TensorProto.FLOAT): TensorDtypeMap(
+        np.dtype("float32"), int(TensorProto.FLOAT), "TensorProto.FLOAT"
+    ),
+    int(TensorProto.UINT8): TensorDtypeMap(
+        np.dtype("uint8"), int(TensorProto.INT32), "TensorProto.UINT8"
+    ),
+    int(TensorProto.INT8): TensorDtypeMap(
+        np.dtype("int8"), int(TensorProto.INT32), "TensorProto.INT8"
+    ),
+    int(TensorProto.UINT16): TensorDtypeMap(
+        np.dtype("uint16"), int(TensorProto.INT32), "TensorProto.UINT16"
+    ),
+    int(TensorProto.INT16): TensorDtypeMap(
+        np.dtype("int16"), int(TensorProto.INT32), "TensorProto.INT16"
+    ),
+    int(TensorProto.INT32): TensorDtypeMap(
+        np.dtype("int32"), int(TensorProto.INT32), "TensorProto.INT32"
+    ),
+    int(TensorProto.INT64): TensorDtypeMap(
+        np.dtype("int64"), int(TensorProto.INT64), "TensorProto.INT64"
+    ),
+    int(TensorProto.BOOL): TensorDtypeMap(
+        np.dtype("bool"), int(TensorProto.INT32), "TensorProto.BOOL"
+    ),
+    int(TensorProto.FLOAT16): TensorDtypeMap(
+        np.dtype("float16"), int(TensorProto.INT32), "TensorProto.FLOAT16"
+    ),
+    int(TensorProto.BFLOAT16): TensorDtypeMap(
+        np.dtype(ml_dtypes.bfloat16),
+        int(TensorProto.INT32),
+        "TensorProto.BFLOAT16",
+    ),
+    int(TensorProto.DOUBLE): TensorDtypeMap(
+        np.dtype("float64"), int(TensorProto.DOUBLE), "TensorProto.DOUBLE"
+    ),
+    int(TensorProto.COMPLEX64): TensorDtypeMap(
+        np.dtype("complex64"), int(TensorProto.FLOAT), "TensorProto.COMPLEX64"
+    ),
+    int(TensorProto.COMPLEX128): TensorDtypeMap(
+        np.dtype("complex128"),
+        int(TensorProto.DOUBLE),
+        "TensorProto.COMPLEX128",
+    ),
+    int(TensorProto.UINT32): TensorDtypeMap(
+        np.dtype("uint32"), int(TensorProto.UINT64), "TensorProto.UINT32"
+    ),
+    int(TensorProto.UINT64): TensorDtypeMap(
+        np.dtype("uint64"), int(TensorProto.UINT64), "TensorProto.UINT64"
+    ),
+    int(TensorProto.STRING): TensorDtypeMap(
+        np.dtype("object"), int(TensorProto.STRING), "TensorProto.STRING"
+    ),
+    int(TensorProto.FLOAT8E4M3FN): TensorDtypeMap(
+        np.dtype(ml_dtypes.float8_e4m3fn),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT8E4M3FN",
+    ),
+    int(TensorProto.FLOAT8E4M3FNUZ): TensorDtypeMap(
+        np.dtype(ml_dtypes.float8_e4m3fnuz),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT8E4M3FNUZ",
+    ),
+    int(TensorProto.FLOAT8E5M2): TensorDtypeMap(
+        np.dtype(ml_dtypes.float8_e5m2),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT8E5M2",
+    ),
+    int(TensorProto.FLOAT8E5M2FNUZ): TensorDtypeMap(
+        np.dtype(ml_dtypes.float8_e5m2fnuz),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT8E5M2FNUZ",
+    ),
+    int(TensorProto.UINT4): TensorDtypeMap(
+        np.dtype(ml_dtypes.uint4), int(TensorProto.INT32), "TensorProto.UINT4"
+    ),
+    int(TensorProto.INT4): TensorDtypeMap(
+        np.dtype(ml_dtypes.int4), int(TensorProto.INT32), "TensorProto.INT4"
+    ),
+    int(TensorProto.FLOAT4E2M1): TensorDtypeMap(
+        np.dtype(ml_dtypes.float4_e2m1fn),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT4E2M1",
+    ),
+    int(TensorProto.FLOAT8E8M0): TensorDtypeMap(
+        np.dtype(ml_dtypes.float8_e8m0fnu),
+        int(TensorProto.INT32),
+        "TensorProto.FLOAT8E8M0",
+    ),
+}
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..de8e3a385434d6c541b8b544342983b60f8eb0c3
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/base.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e77c9fd4bc3971e92ab0d6e0f2ea0bc09df1106
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/base.py
@@ -0,0 +1,148 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from collections import namedtuple
+from typing import TYPE_CHECKING, Any, NewType
+
+import onnx.checker
+import onnx.onnx_cpp2py_export.checker as c_checker
+from onnx import IR_VERSION, ModelProto, NodeProto
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    import numpy
+
+
+class DeviceType:
+    """Describes device type."""
+
+    _Type = NewType("_Type", int)
+    CPU: _Type = _Type(0)
+    CUDA: _Type = _Type(1)
+
+
+class Device:
+    """Describes device type and device id
+    syntax: device_type:device_id(optional)
+    example: 'CPU', 'CUDA', 'CUDA:1'
+    """
+
+    def __init__(self, device: str) -> None:
+        options = device.split(":")
+        self.type = getattr(DeviceType, options[0])
+        self.device_id = 0
+        if len(options) > 1:
+            self.device_id = int(options[1])
+
+
+def namedtupledict(
+    typename: str, field_names: Sequence[str], *args: Any, **kwargs: Any
+) -> type[tuple[Any, ...]]:
+    field_names_map = {n: i for i, n in enumerate(field_names)}
+    # Some output names are invalid python identifier, e.g. "0"
+    kwargs.setdefault("rename", True)
+    data = namedtuple(typename, field_names, *args, **kwargs)  # type: ignore  # noqa: PYI024
+
+    def getitem(self: Any, key: Any) -> Any:
+        if isinstance(key, str):
+            key = field_names_map[key]
+        return super(type(self), self).__getitem__(key)  # type: ignore
+
+    data.__getitem__ = getitem  # type: ignore[assignment]
+    return data
+
+
+class BackendRep:
+    """BackendRep is the handle that a Backend returns after preparing to execute
+    a model repeatedly. Users will then pass inputs to the run function of
+    BackendRep to retrieve the corresponding results.
+    """
+
+    def run(self, inputs: Any, **kwargs: Any) -> tuple[Any, ...]:  # noqa: ARG002
+        """Abstract function."""
+        return (None,)
+
+
+class Backend:
+    """Backend is the entity that will take an ONNX model with inputs,
+    perform a computation, and then return the output.
+
+    For one-off execution, users can use run_node and run_model to obtain results quickly.
+
+    For repeated execution, users should use prepare, in which the Backend
+    does all of the preparation work for executing the model repeatedly
+    (e.g., loading initializers), and returns a BackendRep handle.
+    """
+
+    @classmethod
+    def is_compatible(
+        cls,
+        model: ModelProto,  # noqa: ARG003
+        device: str = "CPU",  # noqa: ARG003
+        **kwargs: Any,  # noqa: ARG003
+    ) -> bool:
+        # Return whether the model is compatible with the backend.
+        return True
+
+    @classmethod
+    def prepare(
+        cls,
+        model: ModelProto,
+        device: str = "CPU",  # noqa: ARG003
+        **kwargs: Any,  # noqa: ARG003
+    ) -> BackendRep | None:
+        # TODO Remove Optional from return type
+        onnx.checker.check_model(model)
+        return None
+
+    @classmethod
+    def run_model(
+        cls, model: ModelProto, inputs: Any, device: str = "CPU", **kwargs: Any
+    ) -> tuple[Any, ...]:
+        backend = cls.prepare(model, device, **kwargs)
+        assert backend is not None
+        return backend.run(inputs)
+
+    @classmethod
+    def run_node(
+        cls,
+        node: NodeProto,
+        inputs: Any,  # noqa: ARG003
+        device: str = "CPU",  # noqa: ARG003
+        outputs_info: (  # noqa: ARG003
+            Sequence[tuple[numpy.dtype, tuple[int, ...]]] | None
+        ) = None,
+        **kwargs: dict[str, Any],
+    ) -> tuple[Any, ...] | None:
+        """Simple run one operator and return the results.
+
+        Args:
+            node: The node proto.
+            inputs: Inputs to the node.
+            device: The device to run on.
+            outputs_info: a list of tuples, which contains the element type and
+                shape of each output. First element of the tuple is the dtype, and
+                the second element is the shape. More use case can be found in
+                https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py
+            kwargs: Other keyword arguments.
+        """
+        # TODO Remove Optional from return type
+        if "opset_version" in kwargs:
+            special_context = c_checker.CheckerContext()
+            special_context.ir_version = IR_VERSION
+            special_context.opset_imports = {"": kwargs["opset_version"]}  # type: ignore
+            onnx.checker.check_node(node, special_context)
+        else:
+            onnx.checker.check_node(node)
+
+        return None
+
+    @classmethod
+    def supports_device(cls, device: str) -> bool:  # noqa: ARG003
+        """Checks whether the backend is compiled with particular device support.
+        In particular it's used in the testing suite.
+        """
+        return True
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1772a77dcab736dd48287b71f55345158068c13f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/__pycache__/__init__.cpython-310.pyc
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe1ffcf1ef81998b3f003d1cfc5c4a28d05b7ebf
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/__init__.py
@@ -0,0 +1,30 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import importlib
+import inspect
+import pkgutil
+import sys
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+
+def collect_sample_implementations() -> dict[str, str]:
+    dict_: dict[str, str] = {}
+    _recursive_scan(sys.modules[__name__], dict_)
+    return dict_
+
+
+def _recursive_scan(package: ModuleType, dict_: dict[str, str]) -> None:
+    pkg_dir = package.__path__
+    module_location = package.__name__
+    for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir):
+        module_name = f"{module_location}.{name}"  # Module/package
+        module = importlib.import_module(module_name)
+        dict_[name] = inspect.getsource(module)
+        if ispkg:
+            _recursive_scan(module, dict_)
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/__pycache__/__init__.cpython-310.pyc
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/abs.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/abs.py
new file mode 100644
index 0000000000000000000000000000000000000000..683743cb7e9fe82b7657c45862ea135dbab3075a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/sample/ops/abs.py
@@ -0,0 +1,8 @@
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+
+def abs(input: np.ndarray) -> np.ndarray:  # noqa: A001
+    return np.abs(input)  # type: ignore[no-any-return]
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cf51d22689cc036a425fa1049450031b59d99dd
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+__all__ = ["BackendTest"]
+# for backward compatibility
+from onnx.backend.test.runner import Runner as BackendTest
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1082362ad99a85db7e6b5a51da14a65dc00b9fea
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import sys
+
+from onnx.backend.test.case.base import Snippets
+from onnx.backend.test.case.utils import import_recursive
+
+
+def collect_snippets() -> dict[str, list[tuple[str, str]]]:
+    import_recursive(sys.modules[__name__])
+    return Snippets
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/base.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1e49f80cc62fe4e57855959f61f256c0a430dc
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/base.py
@@ -0,0 +1,47 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import inspect
+from collections import defaultdict
+from textwrap import dedent
+from typing import Any, ClassVar
+
+import numpy as np
+
+
+def process_snippet(op_name: str, name: str, export: Any) -> tuple[str, str]:
+    snippet_name = name[len("export_") :] or op_name.lower()
+    source_code = dedent(inspect.getsource(export))
+    # remove the function signature line
+    lines = source_code.splitlines()
+    assert lines[0] == "@staticmethod"
+    assert lines[1].startswith("def export")
+    return snippet_name, dedent("\n".join(lines[2:]))
+
+
+Snippets: dict[str, list[tuple[str, str]]] = defaultdict(list)
+
+
+class _Exporter(type):
+    exports: ClassVar[dict[str, list[tuple[str, str]]]] = defaultdict(list)
+
+    def __init__(
+        cls, name: str, bases: tuple[type[Any], ...], dct: dict[str, Any]
+    ) -> None:
+        for k, v in dct.items():
+            if k.startswith("export"):
+                if not isinstance(v, staticmethod):
+                    raise ValueError("Only staticmethods could be named as export.*")
+                export = getattr(cls, k)
+                Snippets[name].append(process_snippet(name, k, export))
+                # export functions should call expect and so populate
+                # TestCases
+                np.random.seed(seed=0)
+                export()
+        super().__init__(name, bases, dct)
+
+
+class Base(metaclass=_Exporter):
+    pass
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/__init__.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..164654787dc9c715f4366ade2d8f7d4be8196e9f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/__init__.py
@@ -0,0 +1,82 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING
+
+from onnx.backend.test.case.test_case import TestCase
+from onnx.backend.test.case.utils import import_recursive
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    import numpy as np
+
+    from onnx import ModelProto
+
+_SimpleModelTestCases = []
+
+
+def expect(
+    model: ModelProto,
+    inputs: Sequence[np.ndarray],
+    outputs: Sequence[np.ndarray],
+    name: str | None = None,
+) -> None:
+    name = name or model.graph.name
+    _SimpleModelTestCases.append(
+        TestCase(
+            name=name,
+            model_name=model.graph.name,
+            url=None,
+            model_dir=None,
+            model=model,
+            data_sets=[(inputs, outputs)],
+            kind="simple",
+            rtol=1e-3,
+            atol=1e-7,
+        )
+    )
+
+
+# BASE_URL = "https://download.onnxruntime.ai/onnx/models"
+BASE_URL = "onnx/backend/test/data/light/light_%s.onnx"
+
+
+def collect_testcases() -> list[TestCase]:
+    """Collect model test cases defined in python/numpy code."""
+    real_model_testcases = []
+
+    model_tests = [
+        ("test_bvlc_alexnet", "bvlc_alexnet", 1e-3, 1e-7),
+        ("test_densenet121", "densenet121", 2e-3, 1e-7),
+        ("test_inception_v1", "inception_v1", 1e-3, 1e-7),
+        ("test_inception_v2", "inception_v2", 1e-3, 1e-7),
+        ("test_resnet50", "resnet50", 1e-3, 1e-7),
+        ("test_shufflenet", "shufflenet", 1e-3, 1e-7),
+        ("test_squeezenet", "squeezenet", 1e-3, 1e-7),
+        ("test_vgg19", "vgg19", 1e-3, 1e-7),
+        ("test_zfnet512", "zfnet512", 1e-3, 1e-7),
+    ]
+
+    for test_name, model_name, rtol, atol in model_tests:
+        url = BASE_URL % model_name
+        real_model_testcases.append(
+            TestCase(
+                name=test_name,
+                model_name=model_name,
+                url=url,
+                model_dir=None,
+                model=None,
+                data_sets=None,
+                kind="real",
+                rtol=rtol,
+                atol=atol,
+            )
+        )
+
+    import_recursive(sys.modules[__name__])
+
+    return real_model_testcases + _SimpleModelTestCases
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/__pycache__/__init__.cpython-310.pyc b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/__pycache__/__init__.cpython-310.pyc
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/expand.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/expand.py
new file mode 100644
index 0000000000000000000000000000000000000000..38f7f47dad1e2f9b4f9880e3d9b0849536219b77
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/expand.py
@@ -0,0 +1,92 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+class ExpandDynamicShape(Base):
+    @staticmethod
+    def export() -> None:
+        def make_graph(
+            node: onnx.helper.NodeProto,
+            input_shape: Sequence[int],
+            shape_shape: Sequence[int],
+            output_shape: Sequence[int],
+        ) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=[node],
+                name="Expand",
+                inputs=[
+                    onnx.helper.make_tensor_value_info(
+                        "X", onnx.TensorProto.FLOAT, input_shape
+                    ),
+                    onnx.helper.make_tensor_value_info(
+                        "shape", onnx.TensorProto.INT64, shape_shape
+                    ),
+                ],
+                outputs=[
+                    onnx.helper.make_tensor_value_info(
+                        "Y", onnx.TensorProto.FLOAT, output_shape
+                    )
+                ],
+            )
+            return graph
+
+        node = onnx.helper.make_node("Expand", ["X", "shape"], ["Y"], name="test")
+        input_shape = [1, 3, 1]
+        x = np.ones(input_shape, dtype=np.float32)
+
+        # 1st testcase
+        shape = np.array([3, 1], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model1")
+
+        # 2nd testcase
+        shape = np.array([1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model2")
+
+        # 3rd testcase
+        shape = np.array([3, 1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model3")
+
+        # 4th testcase
+        shape = np.array([3, 3, 1, 3], dtype=np.int64)
+        y = x * np.ones(shape, dtype=np.float32)
+        graph = make_graph(node, input_shape, shape.shape, y.shape)
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+        expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model4")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/gradient.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/gradient.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e90d63f5c68975017e03c76e04a62ea77b2b73
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/gradient.py
@@ -0,0 +1,110 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN, ONNX_DOMAIN
+
+
+class Gradient(Base):
+    @staticmethod
+    def export_gradient_scalar_add() -> None:
+        add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
+        gradient_node = onnx.helper.make_node(
+            "Gradient",
+            ["a", "b"],
+            ["dc_da", "dc_db"],
+            name="my_gradient",
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+            xs=["a", "b"],
+            y="c",
+        )
+
+        a = np.array(1.0).astype(np.float32)
+        b = np.array(2.0).astype(np.float32)
+        c = a + b
+        # dc / da = d(a+b) / da = 1
+        dc_da = np.array(1).astype(np.float32)
+        # db / db = d(a+b) / db = 1
+        dc_db = np.array(1).astype(np.float32)
+
+        graph = onnx.helper.make_graph(
+            nodes=[add_node, gradient_node],
+            name="GradientOfAdd",
+            inputs=[
+                onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []),
+            ],
+        )
+        opsets = [
+            onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+            onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
+        ]
+        model = onnx.helper.make_model_gen_version(
+            graph, producer_name="backend-test", opset_imports=opsets
+        )
+        expect(
+            model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add"
+        )
+
+    @staticmethod
+    def export_gradient_scalar_add_and_mul() -> None:
+        add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
+        mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul")
+        gradient_node = onnx.helper.make_node(
+            "Gradient",
+            ["a", "b"],
+            ["dd_da", "dd_db"],
+            name="my_gradient",
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+            xs=["a", "b"],
+            y="d",
+        )
+
+        a = np.array(1.0).astype(np.float32)
+        b = np.array(2.0).astype(np.float32)
+        c = a + b
+        # d = a * c = a * (a + b)
+        d = a * c
+        # dd / da = d(a*a+a*b) / da = 2 * a + b
+        dd_da = (2 * a + b).astype(np.float32)
+        # dd / db = d(a*a+a*b) / db = a
+        dd_db = a
+
+        graph = onnx.helper.make_graph(
+            nodes=[add_node, mul_node, gradient_node],
+            name="GradientOfTwoOperators",
+            inputs=[
+                onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []),
+                onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []),
+            ],
+        )
+
+        opsets = [
+            onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
+            onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
+        ]
+        model = onnx.helper.make_model_gen_version(
+            graph, producer_name="backend-test", opset_imports=opsets
+        )
+        expect(
+            model,
+            inputs=[a, b],
+            outputs=[d, dd_da, dd_db],
+            name="test_gradient_of_add_and_mul",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sequence.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..10d773fd7ceb47964e5d7cae9400793a897a3151
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sequence.py
@@ -0,0 +1,457 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import typing
+
+import numpy as np
+
+import onnx
+from onnx import TensorProto
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+
+def SequenceEmptyImpl() -> list[np.ndarray | None]:
+    return []
+
+
+def SequenceConstructImpl(*tensors: np.ndarray) -> list[np.ndarray]:
+    return list(tensors)
+
+
+def SequenceInsertImpl(
+    sequence: list[np.ndarray], tensor: np.ndarray, position: int | None = None
+) -> list[np.ndarray]:
+    if position is None:
+        position = len(sequence)
+    sequence.insert(position, tensor)
+    return sequence
+
+
+def SequenceAtImpl(sequence: list[np.ndarray], position: int) -> np.ndarray:
+    return sequence[position]
+
+
+def SequenceEraseImpl(
+    sequence: list[np.ndarray], position: int | None = None
+) -> list[np.ndarray | None]:
+    if position is None:
+        position = -1
+    del sequence[position]
+    return sequence
+
+
+def SequenceLengthImpl(sequence: list[np.ndarray]) -> np.int64:
+    return np.int64(len(sequence))
+
+
+def SplitToSequenceImpl(
+    tensor: np.ndarray,
+    split: int | list[int] | None = None,
+    axis: int = 0,
+    keepdims: int = 1,
+) -> list[np.ndarray]:
+    dim_size = tensor.shape[axis]
+    if split is None:
+        split = 1
+        split_indices = [
+            i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size
+        ]
+        if not keepdims:
+            results = np.array_split(tensor, split_indices, axis)
+            return [np.squeeze(res, axis) for res in results]
+    if np.isscalar(split):
+        split_indices = [
+            i * split + 1 for i in range(dim_size) if i * split + 1 < dim_size
+        ]
+    else:
+        split_indices = np.cumsum(split) + 1
+    return np.array_split(tensor, split_indices, axis)
+
+
+def ConcatFromSequenceImpl(
+    sequence: list[np.ndarray], axis: int, new_axis: int | None = 0
+) -> np.ndarray:
+    if not new_axis:
+        return np.concatenate(sequence, axis)
+    return np.stack(sequence, axis)
+
+
+class Sequence(Base):
+    @staticmethod
+    def export() -> None:
+        def make_graph(
+            nodes: list[onnx.helper.NodeProto],
+            input_shapes: list[typing.Sequence[str | int] | None],
+            output_shapes: list[typing.Sequence[str | int] | None],
+            input_names: list[str],
+            output_names: list[str],
+            input_types: list[TensorProto.DataType],
+            output_types: list[TensorProto.DataType],
+            initializers: list[TensorProto] | None = None,
+        ) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=nodes,
+                name="Sequence",
+                inputs=[
+                    onnx.helper.make_tensor_value_info(name, input_type, input_shape)
+                    for name, input_type, input_shape in zip(
+                        input_names, input_types, input_shapes
+                    )
+                ],
+                outputs=[
+                    onnx.helper.make_tensor_value_info(name, output_type, output_shape)
+                    for name, output_type, output_shape in zip(
+                        output_names, output_types, output_shapes
+                    )
+                ],
+                initializer=initializers,
+            )
+            return graph
+
+        # 1st testcase - insert and at.
+        # 1. SequenceEmpty:         -> []
+        # 2. SequenceInsert(x):     -> [x]
+        # 3. SequenceInsert(y):     -> [x, y]
+        # 4. SequenceInsert(z, 1):  -> [x, z, y]
+        # 5. SequenceAt(2):         -> y
+        seq_empty_node = onnx.helper.make_node("SequenceEmpty", [], ["Seq_empty"])
+        seq_insert_node = onnx.helper.make_node(
+            "SequenceInsert", ["Seq_empty", "X"], ["Seq_1"]
+        )
+        seq_insert_node2 = onnx.helper.make_node(
+            "SequenceInsert", ["Seq_1", "Y"], ["Seq_2"]
+        )
+        seq_insert_node3 = onnx.helper.make_node(
+            "SequenceInsert", ["Seq_2", "Z", "pos"], ["Seq_3"]
+        )
+        seq_at_node = onnx.helper.make_node("SequenceAt", ["Seq_3", "pos_at"], ["out"])
+
+        x_shape = [2, 3, 4]
+        y_shape = [1, 3, 4]
+        z_shape = [3, 3, 4]
+        out_shape = [None, 3, 4]
+
+        x = np.ones(x_shape, dtype=np.float32)
+        y = np.zeros(y_shape, dtype=np.float32)
+        z = np.ones(z_shape, dtype=np.float32) * 2
+        pos_val = 1
+        pos_at_val = 2
+
+        out = SequenceEmptyImpl()
+        out = SequenceInsertImpl(out, x)
+        out = SequenceInsertImpl(out, y)
+        out = SequenceInsertImpl(out, z, pos_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, y)
+
+        pos = onnx.helper.make_tensor("pos", TensorProto.INT64, (), (pos_val,))
+        pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
+
+        graph = make_graph(
+            [
+                seq_empty_node,
+                seq_insert_node,
+                seq_insert_node2,
+                seq_insert_node3,
+                seq_at_node,
+            ],
+            [x_shape, y_shape, z_shape, [], []],
+            [out_shape],
+            ["X", "Y", "Z", "pos", "pos_at"],
+            ["out"],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2,
+            [onnx.TensorProto.FLOAT],
+            [pos, pos_at],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model1")
+
+        # 2nd testcase - erase and at.
+        # 1. SequenceConstruct(x, y, z):    -> [x, y, z]
+        # 2. SequenceErase(1):              -> [x, z]
+        # 3. SequenceAt(1):                 -> z
+        seq_construct_node = onnx.helper.make_node(
+            "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
+        )
+        seq_erase_node = onnx.helper.make_node(
+            "SequenceErase", ["seq_1", "pos_erase"], ["seq_2"]
+        )
+        seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_2", "pos_at"], ["out"])
+
+        tensor_shape = [2, 3, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        pos_erase_val = 1
+        pos_at_val = 1
+
+        out = SequenceConstructImpl(x, y, z)
+        out = SequenceEraseImpl(out, pos_erase_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, z)
+
+        pos_erase = onnx.helper.make_tensor(
+            "pos_erase", TensorProto.INT64, (), (pos_erase_val,)
+        )
+        pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
+
+        graph = make_graph(
+            [seq_construct_node, seq_erase_node, seq_at_node],
+            [tensor_shape, tensor_shape, tensor_shape, [], []],
+            [tensor_shape],
+            ["X", "Y", "Z", "pos_erase", "pos_at"],
+            ["out"],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 2,
+            [onnx.TensorProto.FLOAT],
+            [pos_erase, pos_at],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model2")
+
+        # 3rd testcase - erase, insert and at, with negative index value.
+        # 1. SequenceConstruct(x, y, z):    -> [x, y, z]
+        # 2. SequenceErase(-3):             -> [y, z]
+        # 3. SequenceInsert(x, -1):         -> [y, x, z]
+        # 4. SequenceAt(-1):                -> z
+        seq_construct_node = onnx.helper.make_node(
+            "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
+        )
+        seq_erase_node = onnx.helper.make_node(
+            "SequenceErase", ["seq_1", "pos_erase"], ["seq_2"]
+        )
+        seq_insert_node = onnx.helper.make_node(
+            "SequenceInsert", ["seq_2", "X", "pos_insert"], ["seq_3"]
+        )
+        seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_3", "pos_at"], ["out"])
+
+        tensor_shape = [2, 3, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        pos_erase_val = -3
+        pos_insert_val = -1
+        pos_at_val = -1
+        out = SequenceConstructImpl(x, y, z)
+        out = SequenceEraseImpl(out, pos_erase_val)
+        out = SequenceInsertImpl(out, x, pos_insert_val)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, z)
+
+        pos_erase = onnx.helper.make_tensor(
+            "pos_erase", TensorProto.INT64, (), (pos_erase_val,)
+        )
+        pos_insert = onnx.helper.make_tensor(
+            "pos_insert", TensorProto.INT64, (), (pos_insert_val,)
+        )
+        pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
+
+        graph = make_graph(
+            [seq_construct_node, seq_erase_node, seq_insert_node, seq_at_node],
+            [tensor_shape, tensor_shape, tensor_shape, [], [], []],
+            [tensor_shape],
+            ["X", "Y", "Z", "pos_erase", "pos_insert", "pos_at"],
+            ["out"],
+            [onnx.TensorProto.FLOAT] * 3 + [onnx.TensorProto.INT64] * 3,
+            [onnx.TensorProto.FLOAT],
+            [pos_erase, pos_insert, pos_at],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(model, inputs=[x, y, z], outputs=[out], name="test_sequence_model3")
+
+        # 4th testcase - concat
+        seq_construct_node = onnx.helper.make_node(
+            "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
+        )
+        seq_concat_node = onnx.helper.make_node(
+            "ConcatFromSequence", ["seq_1"], ["out"], axis=1
+        )
+
+        tensor_shape = [2, 3, 4]
+        concat_out_shape = [2, None, 4]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        out = SequenceConstructImpl(x, y, z)
+        concat_out = ConcatFromSequenceImpl(out, 1)
+
+        graph = make_graph(
+            [seq_construct_node, seq_concat_node],
+            [tensor_shape] * 3,
+            [concat_out_shape],
+            ["X", "Y", "Z"],
+            ["out"],
+            [onnx.TensorProto.FLOAT] * 3,
+            [onnx.TensorProto.FLOAT],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(
+            model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model4"
+        )
+
+        # 5th testcase - concat with new_axis = 1
+        seq_construct_node = onnx.helper.make_node(
+            "SequenceConstruct", ["X", "Y", "Z"], ["seq_1"]
+        )
+        seq_concat_node = onnx.helper.make_node(
+            "ConcatFromSequence", ["seq_1"], ["out"], axis=-1, new_axis=1
+        )
+
+        tensor_shape = [2, 3, 4]
+        concat_out_shape = [2, 3, 4, 3]
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        y = np.zeros(tensor_shape, dtype=np.float32)
+        z = np.ones(tensor_shape, dtype=np.float32) * 2
+        out = SequenceConstructImpl(x, y, z)
+        concat_out = ConcatFromSequenceImpl(out, -1, 1)
+
+        graph = make_graph(
+            [seq_construct_node, seq_concat_node],
+            [tensor_shape] * 3,
+            [concat_out_shape],
+            ["X", "Y", "Z"],
+            ["out"],
+            [onnx.TensorProto.FLOAT] * 3,
+            [onnx.TensorProto.FLOAT],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(
+            model, inputs=[x, y, z], outputs=[concat_out], name="test_sequence_model5"
+        )
+
+        # 6th testcase - split and len
+        seq_split_node = onnx.helper.make_node(
+            "SplitToSequence", ["X"], ["seq_1"], axis=-1
+        )
+        seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"])
+
+        tensor_shape = [2, 3, 4]
+        len_shape = []
+
+        x = np.ones(tensor_shape, dtype=np.float32)
+        out = SplitToSequenceImpl(x, axis=-1)
+        out = SequenceLengthImpl(out)
+        assert np.array_equal(out, np.int64(4))
+
+        graph = onnx.helper.make_graph(
+            nodes=[seq_split_node, seq_len_node],
+            name="Sequence",
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    "X", onnx.TensorProto.FLOAT, tensor_shape
+                )
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    "len", onnx.TensorProto.INT64, len_shape
+                )
+            ],
+        )
+
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(model, inputs=[x], outputs=[out], name="test_sequence_model6")
+
+        # 7th testcase - split with keepdims=0, and SequenceAt
+        seq_split_node = onnx.helper.make_node(
+            "SplitToSequence", ["X"], ["seq_1"], axis=0, keepdims=0
+        )
+        seq_at_node = onnx.helper.make_node("SequenceAt", ["seq_1", "pos_at"], ["out"])
+
+        tensor_shape = [2, 3, 4]
+        out_shape = [3, 4]
+
+        x = np.random.rand(*tensor_shape)
+        pos_at_val = 1
+        out = SplitToSequenceImpl(x, axis=0, keepdims=0)
+        out = SequenceAtImpl(out, pos_at_val)
+        assert np.array_equal(out, x[pos_at_val])
+
+        pos_at = onnx.helper.make_tensor("pos_at", TensorProto.INT64, (), (pos_at_val,))
+
+        graph = make_graph(
+            [seq_split_node, seq_at_node],
+            [tensor_shape, []],
+            [out_shape],
+            ["X", "pos_at"],
+            ["out"],
+            [onnx.TensorProto.DOUBLE, onnx.TensorProto.INT64],
+            [onnx.TensorProto.DOUBLE],
+            [pos_at],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(model, inputs=[x], outputs=[out], name="test_sequence_model7")
+
+        # 8th testcase - split zero length
+        seq_split_node = onnx.helper.make_node(
+            "SplitToSequence", ["X", "Splits"], ["seq_1"]
+        )
+        seq_len_node = onnx.helper.make_node("SequenceLength", ["seq_1"], ["len"])
+
+        tensor_shape = ["n"]
+        splits_shape = [3]
+
+        x = np.array([]).astype(np.float32)
+        splits = np.array([0, 0, 0]).astype(np.int64)
+        out_len = np.int64(3)
+
+        graph = onnx.helper.make_graph(
+            nodes=[seq_split_node, seq_len_node],
+            name="Sequence",
+            inputs=[
+                onnx.helper.make_tensor_value_info(
+                    "X", onnx.TensorProto.FLOAT, tensor_shape
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "Splits", onnx.TensorProto.INT64, splits_shape
+                ),
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info(
+                    "len", onnx.TensorProto.INT64, len_shape
+                )
+            ],
+        )
+
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 12)],
+        )
+        expect(
+            model, inputs=[x, splits], outputs=[out_len], name="test_sequence_model8"
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/shrink.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/shrink.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6e4b3b3c9db76e1789af64b8cd661e9cf38e433
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/shrink.py
@@ -0,0 +1,42 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+
+class ShrinkTest(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Shrink",
+            ["x"],
+            ["y"],
+            lambd=1.5,
+            bias=1.5,
+        )
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name="Shrink",
+            inputs=[
+                onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [5])
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [5])
+            ],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+
+        x = np.array([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=np.float32)
+        y = np.array([-0.5, 0.0, 0.0, 0.0, 0.5], dtype=np.float32)
+
+        expect(model, inputs=[x], outputs=[y], name="test_shrink")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sign.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sign.py
new file mode 100644
index 0000000000000000000000000000000000000000..38e2c112c3225b906d03135e1ce255b711118f98
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/sign.py
@@ -0,0 +1,36 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+
+class SingleSign(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node("Sign", ["x"], ["y"], name="test")
+
+        x = np.array([-1.0, 4.5, -4.5, 3.1, 0.0, 2.4, -5.5]).astype(np.float32)
+        y = np.array([-1.0, 1.0, -1.0, 1.0, 0.0, 1.0, -1.0]).astype(np.float32)
+
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name="SingleSign",
+            inputs=[
+                onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [7])
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [7])
+            ],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+        expect(model, inputs=[x], outputs=[y], name="test_sign_model")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/single-relu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/single-relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c7d605300c5b04840e310f56c039dba713a8845
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/single-relu.py
@@ -0,0 +1,36 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+
+class SingleRelu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node("Relu", ["x"], ["y"], name="test")
+        graph = onnx.helper.make_graph(
+            nodes=[node],
+            name="SingleRelu",
+            inputs=[
+                onnx.helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, [1, 2])
+            ],
+            outputs=[
+                onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [1, 2])
+            ],
+        )
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
+
+        x = np.random.randn(1, 2).astype(np.float32)
+        y = np.maximum(x, 0)
+
+        expect(model, inputs=[x], outputs=[y], name="test_single_relu_model")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/stringnormalizer.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/stringnormalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6e7af57bb12dd6918cd58c758462bfc5ad3a0e2
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/model/stringnormalizer.py
@@ -0,0 +1,206 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.model import expect
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+class NormalizeStrings(Base):
+    @staticmethod
+    def export() -> None:
+        def make_graph(
+            node: onnx.helper.NodeProto,
+            input_shape: Sequence[int],
+            output_shape: Sequence[int],
+        ) -> onnx.helper.GraphProto:
+            graph = onnx.helper.make_graph(
+                nodes=[node],
+                name="StringNormalizer",
+                inputs=[
+                    onnx.helper.make_tensor_value_info(
+                        "x", onnx.TensorProto.STRING, input_shape
+                    )
+                ],
+                outputs=[
+                    onnx.helper.make_tensor_value_info(
+                        "y", onnx.TensorProto.STRING, output_shape
+                    )
+                ],
+            )
+            return graph
+
+        # 1st model_monday_casesensintive_nochangecase
+        stopwords = ["monday"]
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+
+        x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        y = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_monday_casesensintive_nochangecase",
+        )
+
+        # 2nd model_nostopwords_nochangecase
+        node = onnx.helper.make_node(
+            "StringNormalizer", inputs=["x"], outputs=["y"], is_case_sensitive=1
+        )
+
+        x = np.array(["monday", "tuesday"]).astype(object)
+        y = x
+
+        graph = make_graph(node, [2], [2])
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_nostopwords_nochangecase",
+        )
+
+        # 3rd model_monday_casesensintive_lower
+        stopwords = ["monday"]
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="LOWER",
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+
+        x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        y = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_monday_casesensintive_lower",
+        )
+
+        # 4 model_monday_casesensintive_upper
+        stopwords = ["monday"]
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+
+        x = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        y = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object)
+
+        graph = make_graph(node, [4], [3])
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_monday_casesensintive_upper",
+        )
+
+        # 5 monday_insensintive_upper_twodim
+        stopwords = ["monday"]
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            stopwords=stopwords,
+        )
+
+        input_shape = [1, 6]
+        output_shape = [1, 4]
+        x = (
+            np.array(
+                ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"]
+            )
+            .astype(object)
+            .reshape(input_shape)
+        )
+        y = (
+            np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"])
+            .astype(object)
+            .reshape(output_shape)
+        )
+
+        graph = make_graph(node, input_shape, output_shape)
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_monday_insensintive_upper_twodim",
+        )
+
+        # 6 monday_empty_output
+        stopwords = ["monday"]
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            is_case_sensitive=0,
+            stopwords=stopwords,
+        )
+
+        x = np.array(["monday", "monday"]).astype(object)
+        y = np.array([""]).astype(object)
+
+        graph = make_graph(node, [2], [1])
+        model = onnx.helper.make_model_gen_version(
+            graph,
+            producer_name="backend-test",
+            opset_imports=[onnx.helper.make_opsetid("", 10)],
+        )
+        expect(
+            model,
+            inputs=[x],
+            outputs=[y],
+            name="test_strnorm_model_monday_empty_output",
+        )
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/abs.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/abs.py
new file mode 100644
index 0000000000000000000000000000000000000000..23776e82671583fd2594397794456114ccd87405
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/abs.py
@@ -0,0 +1,24 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Abs(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Abs",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.abs(x)
+
+        expect(node, inputs=[x], outputs=[y], name="test_abs")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acos.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acos.py
new file mode 100644
index 0000000000000000000000000000000000000000..79dbe7125936918c3217ab967ca1089f9ada654e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acos.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Acos(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Acos",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arccos(x)
+        expect(node, inputs=[x], outputs=[y], name="test_acos_example")
+
+        x = np.random.rand(3, 4, 5).astype(np.float32)
+        y = np.arccos(x)
+        expect(node, inputs=[x], outputs=[y], name="test_acos")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acosh.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acosh.py
new file mode 100644
index 0000000000000000000000000000000000000000..c67c3044ab984d92837f9a4cc6bcaf34631a7469
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/acosh.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Acosh(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Acosh",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([10, np.e, 1]).astype(np.float32)
+        y = np.arccosh(x)  # expected output [2.99322295,  1.65745449,  0.]
+        expect(node, inputs=[x], outputs=[y], name="test_acosh_example")
+
+        x = np.random.uniform(1.0, 10.0, (3, 4, 5)).astype(np.float32)
+        y = np.arccosh(x)
+        expect(node, inputs=[x], outputs=[y], name="test_acosh")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adagrad.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adagrad.py
new file mode 100644
index 0000000000000000000000000000000000000000..fed01150f09ee711b7d5edc472566ec08c21f05f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adagrad.py
@@ -0,0 +1,116 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
+
+
+def apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor):
+    # Compute adjusted learning-rate.
+    r_ = r / (1 + t * decay_factor)
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Update squared accumulated gradient.
+    h_new = h + g_regularized * g_regularized
+    # Compute ADAGRAD's gradient scaling factors
+    h_sqrt = np.sqrt(h_new) + epsilon
+    # Apply ADAGRAD update rule.
+    x_new = x - r_ * g_regularized / h_sqrt
+    return (x_new.astype(x.dtype), h_new.astype(h.dtype))
+
+
+class Adagrad(Base):
+    @staticmethod
+    def export_adagrad() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        epsilon = 1e-5
+        decay_factor = 0.1
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "Adagrad",
+            inputs=["R", "T", "X", "G", "H"],
+            outputs=["X_new", "H_new"],
+            norm_coefficient=norm_coefficient,
+            epsilon=epsilon,
+            decay_factor=decay_factor,
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.0], dtype=np.float32)
+        g = np.array([-1.0], dtype=np.float32)
+        h = np.array([2.0], dtype=np.float32)
+
+        # Compute expected outputs of Adagrad.
+        x_new, h_new = apply_adagrad(
+            r, t, x, g, h, norm_coefficient, epsilon, decay_factor
+        )
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x, g, h],
+            outputs=[x_new, h_new],
+            name="test_adagrad",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
+
+    @staticmethod
+    def export_adagrad_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        epsilon = 1e-5
+        decay_factor = 0.1
+
+        node = onnx.helper.make_node(
+            "Adagrad",
+            inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
+            outputs=["X1_new", "X2_new", "H1_new", "H2_new"],
+            norm_coefficient=norm_coefficient,
+            epsilon=epsilon,
+            decay_factor=decay_factor,
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        h1 = np.array([2.0], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        h2 = np.array([4.0, 1.0], dtype=np.float32)
+
+        # Compute expected outputs of Adagrad.
+        x1_new, h1_new = apply_adagrad(
+            r, t, x1, g1, h1, norm_coefficient, epsilon, decay_factor
+        )
+        x2_new, h2_new = apply_adagrad(
+            r, t, x2, g2, h2, norm_coefficient, epsilon, decay_factor
+        )
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x1, x2, g1, g2, h1, h2],
+            outputs=[x1_new, x2_new, h1_new, h2_new],
+            name="test_adagrad_multiple",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adam.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..e741839859900c1c0a52883dfba9661149f08091
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/adam.py
@@ -0,0 +1,134 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
+
+
+def apply_adam(
+    r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon
+):
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Update momentum.
+    v_new = alpha * v + (1 - alpha) * g_regularized
+    # Update second-order momentum.
+    h_new = beta * h + (1 - beta) * (g_regularized * g_regularized)
+    # Compute element-wise square root.
+    h_sqrt = np.sqrt(h_new) + epsilon
+    # Adjust learning rate.
+    r_adjusted = None
+    if t > 0:
+        # Consider bias correction on momentums.
+        r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t)
+    else:
+        # No bias correction on momentums.
+        r_adjusted = r
+    # Apply Adam update rule.
+    x_new = x - r_adjusted * (v_new / h_sqrt)
+    # It's possible to apply regularization in the end.
+    x_final = (1 - norm_coefficient_post) * x_new
+    return x_final, v_new, h_new
+
+
+class Adam(Base):
+    @staticmethod
+    def export_adam() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.1
+        epsilon = 1e-7
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "Adam",
+            inputs=["R", "T", "X", "G", "V", "H"],
+            outputs=["X_new", "V_new", "H_new"],
+            norm_coefficient=norm_coefficient,
+            alpha=alpha,
+            beta=beta,
+            epsilon=epsilon,
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+        h = np.array([0.1, 0.1], dtype=np.float32)
+
+        # Compute expected outputs of Adam.
+        x_new, v_new, h_new = apply_adam(
+            r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon
+        )
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x, g, v, h],
+            outputs=[x_new, v_new, h_new],
+            name="test_adam",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
+
+    @staticmethod
+    def export_adam_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.85
+        epsilon = 1e-2
+
+        node = onnx.helper.make_node(
+            "Adam",
+            inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"],
+            outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"],
+            norm_coefficient=norm_coefficient,
+            alpha=alpha,
+            beta=beta,
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        v1 = np.array([2.0], dtype=np.float32)
+        h1 = np.array([0.5], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        v2 = np.array([4.0, 1.0], dtype=np.float32)
+        h2 = np.array([1.0, 10.0], dtype=np.float32)
+
+        # Compute expected outputs of Adam.
+        x1_new, v1_new, h1_new = apply_adam(
+            r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon
+        )
+        x2_new, v2_new, h2_new = apply_adam(
+            r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon
+        )
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
+            outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
+            name="test_adam_multiple",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/add.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/add.py
new file mode 100644
index 0000000000000000000000000000000000000000..36d1e5d015d38e92d7f6981348dc35318910b001
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/add.py
@@ -0,0 +1,60 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Add(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Add",
+            inputs=["x", "y"],
+            outputs=["sum"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_uint64")
+
+    @staticmethod
+    def export_add_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Add",
+            inputs=["x", "y"],
+            outputs=["sum"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        expect(node, inputs=[x, y], outputs=[x + y], name="test_add_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/affinegrid.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/affinegrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..71581fa24fdfbe3fd2a915dbfebab5e86eb43204
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/affinegrid.py
@@ -0,0 +1,210 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_affine_grid import (
+    apply_affine_transform,
+    construct_original_grid,
+)
+
+
+def create_affine_matrix_3d(
+    angle1,
+    angle2,
+    offset_x,
+    offset_y,
+    offset_z,
+    shear_x,
+    shear_y,
+    shear_z,
+    scale_x,
+    scale_y,
+    scale_z,
+):
+    rot_x = np.stack(
+        [
+            np.ones_like(angle1),
+            np.zeros_like(angle1),
+            np.zeros_like(angle1),
+            np.zeros_like(angle1),
+            np.cos(angle1),
+            -np.sin(angle1),
+            np.zeros_like(angle1),
+            np.sin(angle1),
+            np.cos(angle1),
+        ],
+        axis=-1,
+    ).reshape(-1, 3, 3)
+    rot_y = np.stack(
+        [
+            np.cos(angle2),
+            np.zeros_like(angle2),
+            np.sin(angle2),
+            np.zeros_like(angle2),
+            np.ones_like(angle2),
+            np.zeros_like(angle2),
+            -np.sin(angle2),
+            np.zeros_like(angle2),
+            np.cos(angle2),
+        ],
+        axis=-1,
+    ).reshape(-1, 3, 3)
+    shear = np.stack(
+        [
+            np.ones_like(shear_x),
+            shear_x,
+            shear_y,
+            shear_z,
+            np.ones_like(shear_x),
+            shear_x,
+            shear_y,
+            shear_x,
+            np.ones_like(shear_x),
+        ],
+        axis=-1,
+    ).reshape(-1, 3, 3)
+    scale = np.stack(
+        [
+            scale_x,
+            np.zeros_like(scale_x),
+            np.zeros_like(scale_x),
+            np.zeros_like(scale_x),
+            scale_y,
+            np.zeros_like(scale_x),
+            np.zeros_like(scale_x),
+            np.zeros_like(scale_x),
+            scale_z,
+        ],
+        axis=-1,
+    ).reshape(-1, 3, 3)
+    translation = np.transpose(np.array([offset_x, offset_y, offset_z])).reshape(
+        -1, 1, 3
+    )
+    rotation_matrix = rot_y @ rot_x @ shear @ scale  # (N, 3, 3)
+    rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1))
+    affine_matrix = np.hstack((rotation_matrix, translation))
+    affine_matrix = np.transpose(affine_matrix, (0, 2, 1))
+    return affine_matrix.astype(np.float32)
+
+
+def create_affine_matrix_2d(
+    angle1, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y
+):
+    rot = np.stack(
+        [np.cos(angle1), -np.sin(angle1), np.sin(angle1), np.cos(angle1)], axis=-1
+    ).reshape(-1, 2, 2)
+    shear = np.stack(
+        [np.ones_like(shear_x), shear_x, shear_y, np.ones_like(shear_x)], axis=-1
+    ).reshape(-1, 2, 2)
+    scale = np.stack(
+        [scale_x, np.zeros_like(scale_x), np.zeros_like(scale_x), scale_y], axis=-1
+    ).reshape(-1, 2, 2)
+    translation = np.transpose(np.array([offset_x, offset_y])).reshape(-1, 1, 2)
+    rotation_matrix = rot @ shear @ scale  # (N, 3, 3)
+    rotation_matrix = np.transpose(rotation_matrix, (0, 2, 1))
+    affine_matrix = np.hstack((rotation_matrix, translation))
+    affine_matrix = np.transpose(affine_matrix, (0, 2, 1))
+    return affine_matrix.astype(np.float32)
+
+
+def create_theta_2d():
+    angle = np.array([np.pi / 4, np.pi / 3])
+    offset_x = np.array([5.0, 2.5])
+    offset_y = np.array([-3.3, 1.1])
+    shear_x = np.array([-0.5, 0.5])
+    shear_y = np.array([0.3, -0.3])
+    scale_x = np.array([2.2, 1.1])
+    scale_y = np.array([3.1, 0.9])
+    theta_2d = create_affine_matrix_2d(
+        angle, offset_x, offset_y, shear_x, shear_y, scale_x, scale_y
+    )
+    return theta_2d
+
+
+def create_theta_3d():
+    angle1 = np.array([np.pi / 4, np.pi / 3])
+    angle2 = np.array([np.pi / 6, np.pi / 2])
+    offset_x = np.array([5.0, 2.5])
+    offset_y = np.array([-3.3, 1.1])
+    offset_z = np.array([-1.1, 2.2])
+    shear_x = np.array([-0.5, 0.5])
+    shear_y = np.array([0.3, -0.3])
+    shear_z = np.array([0.7, -0.2])
+    scale_x = np.array([2.2, 1.1])
+    scale_y = np.array([3.1, 0.9])
+    scale_z = np.array([0.5, 1.5])
+
+    theta_3d = create_affine_matrix_3d(
+        angle1,
+        angle2,
+        offset_x,
+        offset_y,
+        offset_z,
+        shear_x,
+        shear_y,
+        shear_z,
+        scale_x,
+        scale_y,
+        scale_z,
+    )
+    return theta_3d
+
+
+class AffineGrid(Base):
+    @staticmethod
+    def export_2d_no_reference_evaluator() -> None:
+        theta_2d = create_theta_2d()
+        N, C, H, W = len(theta_2d), 3, 5, 6
+        data_size = (H, W)
+        for align_corners in (0, 1):
+            node = onnx.helper.make_node(
+                "AffineGrid",
+                inputs=["theta", "size"],
+                outputs=["grid"],
+                align_corners=align_corners,
+            )
+
+            original_grid = construct_original_grid(data_size, align_corners)
+            grid = apply_affine_transform(theta_2d, original_grid)
+
+            test_name = "test_affine_grid_2d"
+            if align_corners == 1:
+                test_name += "_align_corners"
+            expect(
+                node,
+                inputs=[theta_2d, np.array([N, C, H, W], dtype=np.int64)],
+                outputs=[grid],
+                name=test_name,
+            )
+
+    @staticmethod
+    def export_3d_no_reference_evaluator() -> None:
+        theta_3d = create_theta_3d()
+        N, C, D, H, W = len(theta_3d), 3, 4, 5, 6
+        data_size = (D, H, W)
+        for align_corners in (0, 1):
+            node = onnx.helper.make_node(
+                "AffineGrid",
+                inputs=["theta", "size"],
+                outputs=["grid"],
+                align_corners=align_corners,
+            )
+
+            original_grid = construct_original_grid(data_size, align_corners)
+            grid = apply_affine_transform(theta_3d, original_grid)
+
+            test_name = "test_affine_grid_3d"
+            if align_corners == 1:
+                test_name += "_align_corners"
+            expect(
+                node,
+                inputs=[theta_3d, np.array([N, C, D, H, W], dtype=np.int64)],
+                outputs=[grid],
+                name=test_name,
+            )
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/label_encoder.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/label_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..41a29e5a9f429565c3fb62fa917b1c321726a566
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/label_encoder.py
@@ -0,0 +1,101 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.helper import make_tensor
+
+
+class LabelEncoder(Base):
+    @staticmethod
+    def export_string_int_label_encoder() -> None:
+        node = onnx.helper.make_node(
+            "LabelEncoder",
+            inputs=["X"],
+            outputs=["Y"],
+            domain="ai.onnx.ml",
+            keys_strings=["a", "b", "c"],
+            values_int64s=[0, 1, 2],
+            default_int64=42,
+        )
+        x = np.array(["a", "b", "d", "c", "g"]).astype(object)
+        y = np.array([0, 1, 42, 2, 42]).astype(np.int64)
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_ai_onnx_ml_label_encoder_string_int",
+        )
+
+        node = onnx.helper.make_node(
+            "LabelEncoder",
+            inputs=["X"],
+            outputs=["Y"],
+            domain="ai.onnx.ml",
+            keys_strings=["a", "b", "c"],
+            values_int64s=[0, 1, 2],
+        )
+        x = np.array(["a", "b", "d", "c", "g"]).astype(object)
+        y = np.array([0, 1, -1, 2, -1]).astype(np.int64)
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_ai_onnx_ml_label_encoder_string_int_no_default",
+        )
+
+    @staticmethod
+    def export_tensor_based_label_encoder() -> None:
+        tensor_keys = make_tensor(
+            "keys_tensor", onnx.TensorProto.STRING, (3,), ["a", "b", "c"]
+        )
+        repeated_string_keys = ["a", "b", "c"]
+        x = np.array(["a", "b", "d", "c", "g"]).astype(object)
+        y = np.array([0, 1, 42, 2, 42]).astype(np.int16)
+
+        node = onnx.helper.make_node(
+            "LabelEncoder",
+            inputs=["X"],
+            outputs=["Y"],
+            domain="ai.onnx.ml",
+            keys_tensor=tensor_keys,
+            values_tensor=make_tensor(
+                "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2]
+            ),
+            default_tensor=make_tensor(
+                "default_tensor", onnx.TensorProto.INT16, (1,), [42]
+            ),
+        )
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_ai_onnx_ml_label_encoder_tensor_mapping",
+        )
+
+        node = onnx.helper.make_node(
+            "LabelEncoder",
+            inputs=["X"],
+            outputs=["Y"],
+            domain="ai.onnx.ml",
+            keys_strings=repeated_string_keys,
+            values_tensor=make_tensor(
+                "values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2]
+            ),
+            default_tensor=make_tensor(
+                "default_tensor", onnx.TensorProto.INT16, (1,), [42]
+            ),
+        )
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_ai_onnx_ml_label_encoder_tensor_value_only_mapping",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/tree_ensemble.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/tree_ensemble.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bd52dfe5b9b18fbda5140b8b41a54762f28cd11
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ai_onnx_ml/tree_ensemble.py
@@ -0,0 +1,123 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.helper import make_tensor
+
+
+class TreeEnsemble(Base):
+    @staticmethod
+    def export_tree_ensemble_single_tree() -> None:
+        node = onnx.helper.make_node(
+            "TreeEnsemble",
+            ["X"],
+            ["Y"],
+            domain="ai.onnx.ml",
+            n_targets=2,
+            membership_values=None,
+            nodes_missing_value_tracks_true=None,
+            nodes_hitrates=None,
+            aggregate_function=1,
+            post_transform=0,
+            tree_roots=[0],
+            nodes_modes=make_tensor(
+                "nodes_modes",
+                onnx.TensorProto.UINT8,
+                (3,),
+                np.array([0, 0, 0], dtype=np.uint8),
+            ),
+            nodes_featureids=[0, 0, 0],
+            nodes_splits=make_tensor(
+                "nodes_splits",
+                onnx.TensorProto.DOUBLE,
+                (3,),
+                np.array([3.14, 1.2, 4.2], dtype=np.float64),
+            ),
+            nodes_truenodeids=[1, 0, 1],
+            nodes_trueleafs=[0, 1, 1],
+            nodes_falsenodeids=[2, 2, 3],
+            nodes_falseleafs=[0, 1, 1],
+            leaf_targetids=[0, 1, 0, 1],
+            leaf_weights=make_tensor(
+                "leaf_weights",
+                onnx.TensorProto.DOUBLE,
+                (4,),
+                np.array([5.23, 12.12, -12.23, 7.21], dtype=np.float64),
+            ),
+        )
+
+        x = np.array([1.2, 3.4, -0.12, 1.66, 4.14, 1.77], np.float64).reshape(3, 2)
+        y = np.array([[5.23, 0], [5.23, 0], [0, 12.12]], dtype=np.float64)
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_ai_onnx_ml_tree_ensemble_single_tree",
+        )
+
+    @staticmethod
+    def export_tree_ensemble_set_membership() -> None:
+        node = onnx.helper.make_node(
+            "TreeEnsemble",
+            ["X"],
+            ["Y"],
+            domain="ai.onnx.ml",
+            n_targets=4,
+            aggregate_function=1,
+            membership_values=make_tensor(
+                "membership_values",
+                onnx.TensorProto.FLOAT,
+                (8,),
+                [1.2, 3.7, 8, 9, np.nan, 12, 7, np.nan],
+            ),
+            nodes_missing_value_tracks_true=None,
+            nodes_hitrates=None,
+            post_transform=0,
+            tree_roots=[0],
+            nodes_modes=make_tensor(
+                "nodes_modes",
+                onnx.TensorProto.UINT8,
+                (3,),
+                np.array([0, 6, 6], dtype=np.uint8),
+            ),
+            nodes_featureids=[0, 0, 0],
+            nodes_splits=make_tensor(
+                "nodes_splits",
+                onnx.TensorProto.FLOAT,
+                (3,),
+                np.array([11, 232344.0, np.nan], dtype=np.float32),
+            ),
+            nodes_trueleafs=[0, 1, 1],
+            nodes_truenodeids=[1, 0, 1],
+            nodes_falseleafs=[1, 0, 1],
+            nodes_falsenodeids=[2, 2, 3],
+            leaf_targetids=[0, 1, 2, 3],
+            leaf_weights=make_tensor(
+                "leaf_weights", onnx.TensorProto.FLOAT, (4,), [1, 10, 1000, 100]
+            ),
+        )
+
+        x = np.array([1.2, 3.4, -0.12, np.nan, 12, 7], np.float32).reshape(-1, 1)
+        expected = np.array(
+            [
+                [1, 0, 0, 0],
+                [0, 0, 0, 100],
+                [0, 0, 0, 100],
+                [0, 0, 1000, 0],
+                [0, 0, 1000, 0],
+                [0, 10, 0, 0],
+            ],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[x],
+            outputs=[expected],
+            name="test_ai_onnx_ml_tree_ensemble_set_membership",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/and.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/and.py
new file mode 100644
index 0000000000000000000000000000000000000000..dda42aff372e4255d1e553a80c8200444411fef0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/and.py
@@ -0,0 +1,76 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class And(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "And",
+            inputs=["x", "y"],
+            outputs=["and"],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        y = (np.random.randn(3, 4) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and2d")
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and3d")
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and4d")
+
+    @staticmethod
+    def export_and_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "And",
+            inputs=["x", "y"],
+            outputs=["and"],
+        )
+
+        # 3d vs 1d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v1d")
+
+        # 3d vs 2d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(4, 5) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast3v2d")
+
+        # 4d vs 2d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v2d")
+
+        # 4d vs 3d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v3d")
+
+        # 4d vs 4d
+        x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+        z = np.logical_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_and_bcast4v4d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmax.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..171a6cc8d776e4c399822b1808765da771031036
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmax.py
@@ -0,0 +1,256 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def argmax_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray:
+    result = np.argmax(data, axis=axis)
+    if keepdims == 1:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+def argmax_use_numpy_select_last_index(
+    data: np.ndarray, axis: int = 0, keepdims: int = True
+) -> np.ndarray:
+    data = np.flip(data, axis)
+    result = np.argmax(data, axis=axis)
+    result = data.shape[axis] - result - 1
+    if keepdims:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+class ArgMax(Base):
+    @staticmethod
+    def export_no_keepdims() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # result: [0, 1]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_no_keepdims_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmax_no_keepdims_random"
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # result: [[0], [1]]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmax_keepdims_example"
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmax_keepdims_random"
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax", inputs=["data"], outputs=["result"], keepdims=keepdims
+        )
+
+        # result: [[1, 1]]
+        result = argmax_use_numpy(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_default_axis_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmax_use_numpy(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_default_axis_random",
+        )
+
+    @staticmethod
+    def export_negative_axis_keepdims() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # result: [[0], [1]]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_negative_axis_keepdims_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_negative_axis_keepdims_random",
+        )
+
+    @staticmethod
+    def export_no_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            "ArgMax",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [1, 1]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_no_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_no_keepdims_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [[1], [1]]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_keepdims_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax",
+            inputs=["data"],
+            outputs=["result"],
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+
+        # result: [[1, 1]]
+        result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_default_axis_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_default_axis_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_negative_axis_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMax",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [[1], [1]]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_negative_axis_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmax_negative_axis_keepdims_random_select_last_index",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmin.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmin.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b1bf2e959a999ecc5edd8ab124e94b988b076a0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/argmin.py
@@ -0,0 +1,256 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def argmin_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray:
+    result = np.argmin(data, axis=axis)
+    if keepdims == 1:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+def argmin_use_numpy_select_last_index(
+    data: np.ndarray, axis: int = 0, keepdims: int = True
+) -> np.ndarray:
+    data = np.flip(data, axis)
+    result = np.argmin(data, axis=axis)
+    result = data.shape[axis] - result - 1
+    if keepdims:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+class ArgMin(Base):
+    @staticmethod
+    def export_no_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # The content of result is : [[1, 0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_no_keepdims_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random"
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # The content of result is : [[1], [0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example"
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random"
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims
+        )
+
+        # The content of result is : [[0], [0]]
+        result = argmin_use_numpy(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_default_axis_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmin_use_numpy(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_default_axis_random",
+        )
+
+    @staticmethod
+    def export_negative_axis_keepdims() -> None:
+        data = np.array([[2, 1], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
+        )
+        # The content of result is : [[1], [0]]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_negative_axis_keepdims_example",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_negative_axis_keepdims_random",
+        )
+
+    @staticmethod
+    def export_no_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 0
+        node = onnx.helper.make_node(
+            "ArgMin",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [[1, 0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_no_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 4]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_no_keepdims_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = 1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [[1], [0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 1, 4]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_keepdims_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin",
+            inputs=["data"],
+            outputs=["result"],
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+
+        # result: [[0, 0]]
+        result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_default_axis_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [1, 3, 4]
+        result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_default_axis_random_select_last_index",
+        )
+
+    @staticmethod
+    def export_negative_axis_keepdims_select_last_index() -> None:
+        data = np.array([[2, 2], [3, 10]], dtype=np.float32)
+        axis = -1
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ArgMin",
+            inputs=["data"],
+            outputs=["result"],
+            axis=axis,
+            keepdims=keepdims,
+            select_last_index=True,
+        )
+        # result: [[1], [0]]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_negative_axis_keepdims_example_select_last_index",
+        )
+
+        data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
+        # result's shape: [2, 3, 1]
+        result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
+        expect(
+            node,
+            inputs=[data],
+            outputs=[result],
+            name="test_argmin_negative_axis_keepdims_random_select_last_index",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asin.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asin.py
new file mode 100644
index 0000000000000000000000000000000000000000..e46cdf3715f4f4fc92f269f40f6b6964570506c6
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asin.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Asin(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Asin",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arcsin(x)
+        expect(node, inputs=[x], outputs=[y], name="test_asin_example")
+
+        x = np.random.rand(3, 4, 5).astype(np.float32)
+        y = np.arcsin(x)
+        expect(node, inputs=[x], outputs=[y], name="test_asin")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asinh.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asinh.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4674cf3b171c924ffdb6c5d09030b19a6d3c3b3
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/asinh.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Asinh(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Asinh",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.arcsinh(x)  # expected output [-0.88137358,  0.,  0.88137358]
+        expect(node, inputs=[x], outputs=[y], name="test_asinh_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.arcsinh(x)
+        expect(node, inputs=[x], outputs=[y], name="test_asinh")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atan.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atan.py
new file mode 100644
index 0000000000000000000000000000000000000000..8eaef5a5986ac2bcb4182a1586a3d89095bb3f37
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atan.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Atan(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Atan",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.arctan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_atan_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.arctan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_atan")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atanh.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atanh.py
new file mode 100644
index 0000000000000000000000000000000000000000..57be78489921e0474946e85de51d86e1a8a75ce4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/atanh.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Atanh(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Atanh",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        y = np.arctanh(x)  # expected output [-0.54930615,  0.,  0.54930615]
+        expect(node, inputs=[x], outputs=[y], name="test_atanh_example")
+
+        x = np.random.uniform(0.0, 1.0, (3, 4, 5)).astype(np.float32)
+        y = np.arctanh(x)
+        expect(node, inputs=[x], outputs=[y], name="test_atanh")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/attention.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..a26bd2691640006e7abe2fe73f160aece3743f40
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/attention.py
@@ -0,0 +1,1874 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_attention import _compute_attention
+
+
+class Attention(Base):
+    @staticmethod
+    def export_attention() -> None:
+        node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"])
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d",
+        )
+
+    @staticmethod
+    def export_attention_fp16() -> None:
+        node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"])
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float16)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float16)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float16)
+
+        Y, _, _, _ = _compute_attention(Q, K, V)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_fp16",
+        )
+
+    @staticmethod
+    def export_attention_gqa() -> None:
+        node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"])
+
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_gqa",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes() -> None:
+        node = onnx.helper.make_node("Attention", inputs=["Q", "K", "V"], outputs=["Y"])
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_sizes",
+        )
+
+    @staticmethod
+    def export_attention_scaled() -> None:
+        scale = 1e-2
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, scale=scale)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_scaled",
+        )
+
+    @staticmethod
+    def export_attention_gqa_scaled() -> None:
+        scale = 1e-2
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+        )
+
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, scale=scale)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_gqa_scaled",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_scaled() -> None:
+        scale = 1e-2
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, scale=scale)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_sizes_scaled",
+        )
+
+    @staticmethod
+    def export_attention_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_causal",
+        )
+
+    @staticmethod
+    def export_attention_gqa_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+        )
+
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, is_causal=1)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_gqa_causal",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            is_causal=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_sizes_causal",
+        )
+
+    @staticmethod
+    def export_attention_attn_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_attn_3d_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_3d",
+        )
+
+    @staticmethod
+    def export_attention_attn_3d_mask_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+            is_causal=1,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 1, 4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            is_causal=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_3d_causal",
+        )
+
+    @staticmethod
+    def export_attention_attn_4d_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_4d",
+        )
+
+    @staticmethod
+    def export_attention_attn_4d_mask_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+            is_causal=1,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            is_causal=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_4d_causal",
+        )
+
+    @staticmethod
+    def export_attention_attn_mask_bool() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(bool)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_bool",
+        )
+
+    @staticmethod
+    def export_attention_attn_mask_bool_4d() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6).astype(bool)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_attn_mask_bool_4d",
+        )
+
+    @staticmethod
+    def export_attention_gqa_attn_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_gqa_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_attn_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_sizes_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_gqa_with_past_and_present() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_gqa_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_gqa_with_past_and_present_fp16() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float16)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float16)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float16)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float16)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float16)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_gqa_with_past_and_present_fp16",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_with_past_and_present() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_diff_heads_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_with_past_and_present_mask3D() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+        attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_diff_heads_with_past_and_present_mask3d",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_with_past_and_present_mask4D() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_4d_diff_heads_with_past_and_present_mask4d",
+        )
+
+    @staticmethod
+    def export_attention_softcap() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=2.0,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_softcap",
+        )
+
+    @staticmethod
+    def export_attention_gqa_softcap() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=2.0,
+        )
+
+        Q = np.random.rand(2, 9, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(Q, K, V, softcap=2.0)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_gqa_softcap",
+        )
+
+    @staticmethod
+    def export_attention_diff_head_sizes_softcap() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=2.0,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            softcap=2.0,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_sizes_softcap",
+        )
+
+    @staticmethod
+    def export_attention_with_qk_matmul() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y", "", "", "qk_matmul_output"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+
+        Y, _, _, qk_matmul_output = _compute_attention(Q, K, V)
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y, qk_matmul_output],
+            name="test_attention_4d_with_qk_matmul",
+        )
+
+    @staticmethod
+    def export_attention_with_qk_matmul_bias() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y", "", "", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            qk_matmul_output_mode=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y, qk_matmul_output],
+            name="test_attention_4d_with_qk_matmul_bias",
+        )
+
+    @staticmethod
+    def export_attention_with_qk_matmul_softcap() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y", "", "", "qk_matmul_output"],
+            softcap=2.0,
+            qk_matmul_output_mode=2,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            softcap=2.0,
+            qk_matmul_output_mode=2,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y, qk_matmul_output],
+            name="test_attention_4d_with_qk_matmul_softcap",
+        )
+
+    @staticmethod
+    def export_attention_with_qk_matmul_softmax() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y", "", "", "qk_matmul_output"],
+            qk_matmul_output_mode=3,
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            qk_matmul_output_mode=3,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y, qk_matmul_output],
+            name="test_attention_4d_with_qk_matmul_softmax",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul_bias() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            qk_matmul_output_mode=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul_bias",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul_bias_3d_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            qk_matmul_output_mode=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul_bias_4d_mask() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            qk_matmul_output_mode=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul_bias_3d_mask_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+            is_causal=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 1, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            qk_matmul_output_mode=1,
+            is_causal=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul_bias_3d_mask_causal",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul_bias_4d_mask_causal() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            qk_matmul_output_mode=1,
+            is_causal=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            qk_matmul_output_mode=1,
+            is_causal=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul_bias_4d_mask_causal",
+        )
+
+    @staticmethod
+    def export_attention_with_past_and_present_qk_matmul() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_4d_with_past_and_present_qk_matmul",
+        )
+
+    @staticmethod
+    def export_attention_3d() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa() -> None:
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_gqa",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_diff_heads_sizes",
+        )
+
+    @staticmethod
+    def export_attention_3d_scaled() -> None:
+        scale = 1e-2
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_scaled",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa_scaled() -> None:
+        scale = 1e-2
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_gqa_scaled",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes_scaled() -> None:
+        scale = 1e-2
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            scale=scale,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_diff_heads_sizes_scaled",
+        )
+
+    @staticmethod
+    def export_attention_3d_causal() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_causal",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa_causal() -> None:
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_gqa_causal",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes_causal() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            is_causal=1,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_diff_heads_sizes_causal",
+        )
+
+    @staticmethod
+    def export_attention_3d_attn_mask() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_3d_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa_attn_mask() -> None:
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_3d_gqa_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes_attn_mask() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+        attn_mask = np.random.rand(4, 6).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask],
+            outputs=[Y],
+            name="test_attention_3d_diff_heads_sizes_attn_mask",
+        )
+
+    @staticmethod
+    def export_attention_3d_softcap() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_softcap",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa_softcap() -> None:
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_gqa_softcap",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes_softcap() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            softcap=3.0,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_diff_heads_sizes_softcap",
+        )
+
+    @staticmethod
+    def export_attention_3d_with_past_and_present() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_3d_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_3d_gqa_with_past_and_present() -> None:
+        q_num_heads, kv_num_heads = 9, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 72).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_3d_gqa_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_3d_diff_head_sizes_with_past_and_present() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 30).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32)
+
+        Y, present_key, present_value, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value],
+            name="test_attention_3d_diff_heads_with_past_and_present",
+        )
+
+    @staticmethod
+    def export_attention_3d_with_past_and_present_qk_matmul() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_3d_with_past_and_present_qk_matmul",
+        )
+
+    @staticmethod
+    def export_attention_3d_with_past_and_present_qk_matmul_bias() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            qk_matmul_output_mode=1,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            qk_matmul_output_mode=1,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_3d_with_past_and_present_qk_matmul_bias",
+        )
+
+    @staticmethod
+    def export_attention_3d_with_past_and_present_qk_matmul_softcap() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            softcap=2.0,
+            qk_matmul_output_mode=2,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            softcap=2.0,
+            qk_matmul_output_mode=2,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_3d_with_past_and_present_qk_matmul_softcap",
+        )
+
+    @staticmethod
+    def export_attention_3d_with_past_and_present_qk_matmul_softmax() -> None:
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "past_key", "past_value"],
+            outputs=["Y", "present_key", "present_value", "qk_matmul_output"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            qk_matmul_output_mode=3,
+        )
+
+        past_sequence_length = 12
+        Q = np.random.rand(2, 4, 24).astype(np.float32)
+        K = np.random.rand(2, 6, 24).astype(np.float32)
+        V = np.random.rand(2, 6, 24).astype(np.float32)
+        attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32)
+        past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+        past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32)
+
+        Y, present_key, present_value, qk_matmul_output = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            past_key=past_key,
+            past_value=past_value,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+            qk_matmul_output_mode=3,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, past_key, past_value],
+            outputs=[Y, present_key, present_value, qk_matmul_output],
+            name="test_attention_3d_with_past_and_present_qk_matmul_softmax",
+        )
+
+    @staticmethod
+    def export_attention_3d_transpose_verification() -> None:
+        """Test case to verify correct 3D to 4D transpose behavior.
+
+        This test verifies that 3D inputs are correctly reshaped and transposed
+        according to the ONNX specification:
+        [batch_size, seq_length, hidden_size] ->
+        [batch_size, seq_length, num_heads, head_size] ->
+        [batch_size, num_heads, seq_length, head_size]
+        """
+        q_num_heads, kv_num_heads = 3, 3
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V"],
+            outputs=["Y"],
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        # Test inputs that will clearly demonstrate the transpose behavior
+        batch_size = 1
+        q_seq_length = 2
+        kv_seq_length = 2
+        head_size = 4
+        q_hidden_size = q_num_heads * head_size  # 3 * 4 = 12
+        kv_hidden_size = kv_num_heads * head_size  # 3 * 4 = 12
+
+        # Create structured inputs to verify correct transpose behavior
+        # Q has a pattern where each position in hidden dimension has a specific value
+        Q = np.zeros((batch_size, q_seq_length, q_hidden_size), dtype=np.float32)
+        # Fill Q with pattern: head0=[1,1,1,1], head1=[2,2,2,2], head2=[3,3,3,3]
+        for head in range(q_num_heads):
+            start_idx = head * head_size
+            end_idx = start_idx + head_size
+            Q[0, :, start_idx:end_idx] = float(head + 1)
+
+        K = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1
+        V = np.ones((batch_size, kv_seq_length, kv_hidden_size), dtype=np.float32) * 0.1
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            q_num_heads=q_num_heads,
+            kv_num_heads=kv_num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V],
+            outputs=[Y],
+            name="test_attention_3d_transpose_verification",
+        )
+
+    @staticmethod
+    def export_attention_4d_diff_heads_mask4d_padded_kv() -> None:
+        node = onnx.helper.make_node(
+            "Attention",
+            inputs=["Q", "K", "V", "attn_mask", "", "", "nonpad_kv_seqlen"],
+            outputs=["Y"],
+        )
+
+        Q = np.random.rand(2, 3, 4, 8).astype(np.float32)
+        K = np.random.rand(2, 3, 6, 8).astype(np.float32)
+        V = np.random.rand(2, 3, 6, 10).astype(np.float32)
+        attn_mask = np.random.rand(2, 3, 4, 4).astype(np.float32)
+        nonpad_kv_seqlen = np.array([3, 4], dtype=np.int64)
+
+        Y, _, _, _ = _compute_attention(
+            Q,
+            K,
+            V,
+            attn_mask=attn_mask,
+            nonpad_kv_seqlen=nonpad_kv_seqlen,
+        )
+
+        expect(
+            node,
+            inputs=[Q, K, V, attn_mask, nonpad_kv_seqlen],
+            outputs=[Y],
+            name="test_attention_4d_diff_heads_mask4d_padded_kv",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/averagepool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/averagepool.py
new file mode 100644
index 0000000000000000000000000000000000000000..22b806a63f6dd9b2f424cb8c07caacc1d625a373
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/averagepool.py
@@ -0,0 +1,695 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_pool_common import (
+    get_output_shape_auto_pad,
+    get_output_shape_explicit_padding,
+    get_pad_shape,
+    pool,
+)
+
+
+class AveragePool(Base):
+    @staticmethod
+    def export_averagepool_2d_precomputed_pads() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array(
+            [
+                [
+                    [
+                        [7, 7.5, 8, 8.5, 9],
+                        [9.5, 10, 10.5, 11, 11.5],
+                        [12, 12.5, 13, 13.5, 14],
+                        [14.5, 15, 15.5, 16, 16.5],
+                        [17, 17.5, 18, 18.5, 19],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        expect(
+            node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads"
+        )
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_pads_count_include_pad() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+            count_include_pad=1,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array(
+            [
+                [
+                    [
+                        [2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
+                        [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
+                        [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
+                        [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
+                        [6.1200, 8.4000, 10.8000, 8.8800, 6.8400],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_averagepool_2d_precomputed_pads_count_include_pad",
+        )
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_strides() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_averagepool_2d_precomputed_strides",
+        )
+
+    @staticmethod
+    def export_averagepool_2d_precomputed_same_upper() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 3, 3]
+        pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            auto_pad="SAME_UPPER",
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype(
+            np.float32
+        )
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_averagepool_2d_precomputed_same_upper",
+        )
+
+    @staticmethod
+    def export_averagepool_1d_default() -> None:
+        """input_shape: [1, 3, 32]
+        output_shape: [1, 3, 31]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2],
+        )
+        x = np.random.randn(1, 3, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = [2]
+        strides = [1]
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default")
+
+    @staticmethod
+    def export_averagepool_2d_default() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default")
+
+    @staticmethod
+    def export_averagepool_3d_default() -> None:
+        """input_shape: [1, 3, 32, 32, 32]
+        output_shape: [1, 3, 31, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default")
+
+    @staticmethod
+    def export_averagepool_2d_same_upper() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_UPPER",
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_top = pad_shape[0] // 2
+        pad_bottom = pad_shape[0] - pad_top
+        pad_left = pad_shape[1] // 2
+        pad_right = pad_shape[1] - pad_left
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        pads = (pad_top, pad_left, pad_bottom, pad_right)
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "AVG",
+            pads_required=pads,
+            pads=pads,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")
+
+    @staticmethod
+    def export_averagepool_2d_same_lower() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_LOWER",
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_bottom = pad_shape[0] // 2
+        pad_top = pad_shape[0] - pad_bottom
+        pad_right = pad_shape[1] // 2
+        pad_left = pad_shape[1] - pad_right
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        pads = (pad_top, pad_left, pad_bottom, pad_right)
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "AVG",
+            pads_required=pads,
+            pads=pads,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")
+
+    @staticmethod
+    def export_averagepool_2d_pads() -> None:
+        """input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = 2
+        pad_top = 2
+        pad_right = 2
+        pad_left = 2
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        out_shape, extra_pads = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides, ceil_mode=False
+        )
+        padded = np.pad(
+            x,
+            (
+                (0, 0),
+                (0, 0),
+                (extra_pads[0], extra_pads[2]),
+                (extra_pads[1], extra_pads[3]),
+            ),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "AVG",
+            pads_required=extra_pads,
+            pads=pads,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")
+
+    @staticmethod
+    def export_averagepool_2d_pads_count_include_pad() -> None:
+        """input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2],
+            count_include_pad=1,
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        dilations = (1, 1)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = 2
+        pad_top = 2
+        pad_right = 2
+        pad_left = 2
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        out_shape, extra_pads = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False
+        )
+        padded = np.pad(
+            x,
+            (
+                (0, 0),
+                (0, 0),
+                (extra_pads[0], extra_pads[2]),
+                (extra_pads[1], extra_pads[3]),
+            ),
+            mode="constant",
+            constant_values=0,
+        )
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "AVG",
+            pads_required=extra_pads,
+            pads=pads,
+            count_include_pad=1,
+        )
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_averagepool_2d_pads_count_include_pad",
+        )
+
+    @staticmethod
+    def export_averagepool_2d_strides() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 10, 10]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            strides=[3, 3],
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (5, 5)
+        strides = (3, 3)
+        out_shape, pads = get_output_shape_explicit_padding(
+            None, x_shape[2:], kernel_shape, strides, ceil_mode=False
+        )
+        padded = x
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "AVG",
+            pads_required=pads,
+            pads=None,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")
+
+    @staticmethod
+    def export_averagepool_2d_ceil() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            ceil_mode=True,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")
+
+    @staticmethod
+    def export_averagepool_2d_ceil_last_window_starts_on_pad() -> None:
+        """input_shape: [1, 3, 2, 2]
+        output_shape: [1, 3, 1, 1]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            strides=[3, 3],
+            pads=[1, 1, 1, 1],
+            ceil_mode=True,
+            count_include_pad=1,
+        )
+        x = np.array(
+            [
+                [
+                    [[0.8580, 0.0786], [0.2692, 0.1537]],
+                    [[0.8816, 0.4353], [0.5772, 0.6623]],
+                    [[0.9067, 0.9483], [0.5970, 0.7630]],
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[0.1511]], [[0.2841]], [[0.3572]]]]).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_averagepool_2d_ceil_last_window_starts_on_pad",
+        )
+
+    @staticmethod
+    def export_averagepool_2d_dilations() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[1, 1],
+            dilations=[2, 2],
+            ceil_mode=True,
+        )
+
+        # input shape: [1, 1, 4, 4]
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations")
+
+    @staticmethod
+    def export_averagepool_3d_dilations() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "AveragePool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+            strides=[1, 1, 1],
+            dilations=[2, 2, 2],
+            ceil_mode=True,
+        )
+
+        # input shape: [1, 1, 4, 4, 4]
+        x = np.array(
+            [
+                [
+                    [
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32)
+
+        expect(
+            node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small"
+        )
+
+    @staticmethod
+    def export_averagepool_3d_dilations_large() -> None:
+        x_shape = (32, 32, 32)
+        dilations = (2, 2, 2)
+        kernel_shape = (5, 5, 5)
+        strides = (3, 3, 3)
+        count_include_pad = 0
+
+        for count_include_pad in (0, 1):
+            for ceil_mode in (True, False):
+                node = onnx.helper.make_node(
+                    "AveragePool",
+                    inputs=["x"],
+                    outputs=["y"],
+                    kernel_shape=kernel_shape,
+                    strides=strides,
+                    dilations=dilations,
+                    count_include_pad=count_include_pad,
+                    ceil_mode=ceil_mode,
+                )
+
+                x = np.random.randn(1, 1, *x_shape).astype(np.float32)
+                out_shape, extra_pads = get_output_shape_explicit_padding(
+                    None,
+                    x_shape,
+                    kernel_shape,
+                    strides,
+                    dilations=dilations,
+                    ceil_mode=ceil_mode,
+                )
+                padded = np.pad(
+                    x,
+                    (
+                        (0, 0),
+                        (0, 0),
+                        (extra_pads[0], extra_pads[3]),
+                        (extra_pads[1], extra_pads[4]),
+                        (extra_pads[2], extra_pads[5]),
+                    ),
+                    mode="constant",
+                    constant_values=0 if count_include_pad == 1 else np.nan,
+                )
+                y = pool(
+                    padded,
+                    (1, 1, *x_shape),
+                    kernel_shape,
+                    strides,
+                    out_shape,
+                    "AVG",
+                    pads_required=extra_pads,
+                    pads=None,
+                    dilations=dilations,
+                    count_include_pad=count_include_pad,
+                )
+
+                test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}"
+                expect(node, inputs=[x], outputs=[y], name=test_name)
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/batchnorm.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4b05fe9eaa36c3c54803ed479578b363968334a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/batchnorm.py
@@ -0,0 +1,137 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def _batchnorm_test_mode(x, s, bias, mean, var, epsilon=1e-5):
+    dims_x = len(x.shape)
+    dim_ones = (1,) * (dims_x - 2)
+    s = s.reshape(-1, *dim_ones)
+    bias = bias.reshape(-1, *dim_ones)
+    mean = mean.reshape(-1, *dim_ones)
+    var = var.reshape(-1, *dim_ones)
+    return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+
+def _batchnorm_training_mode(x, s, bias, mean, var, momentum=0.9, epsilon=1e-5):
+    axis = tuple(np.delete(np.arange(len(x.shape)), 1))
+    saved_mean = x.mean(axis=axis)
+    saved_var = x.var(axis=axis)
+    output_mean = mean * momentum + saved_mean * (1 - momentum)
+    output_var = var * momentum + saved_var * (1 - momentum)
+    y = _batchnorm_test_mode(x, s, bias, saved_mean, saved_var, epsilon=epsilon)
+    return y.astype(np.float32), output_mean, output_var
+
+
+class BatchNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        y = _batchnorm_test_mode(x, s, bias, mean, var).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "BatchNormalization",
+            inputs=["x", "s", "bias", "mean", "var"],
+            outputs=["y"],
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(
+            node,
+            inputs=[x, s, bias, mean, var],
+            outputs=[y],
+            name="test_batchnorm_example",
+        )
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        epsilon = 1e-2
+        y = _batchnorm_test_mode(x, s, bias, mean, var, epsilon).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "BatchNormalization",
+            inputs=["x", "s", "bias", "mean", "var"],
+            outputs=["y"],
+            epsilon=epsilon,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(
+            node,
+            inputs=[x, s, bias, mean, var],
+            outputs=[y],
+            name="test_batchnorm_epsilon",
+        )
+
+    @staticmethod
+    def export_train() -> None:
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        # using np.bool(1) while generating test data with "'bool' object has no attribute 'dtype'"
+        # working around by using np.byte(1).astype(bool)
+        training_mode = 1
+        y, output_mean, output_var = _batchnorm_training_mode(x, s, bias, mean, var)
+
+        node = onnx.helper.make_node(
+            "BatchNormalization",
+            inputs=["x", "s", "bias", "mean", "var"],
+            outputs=["y", "output_mean", "output_var"],
+            training_mode=training_mode,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(
+            node,
+            inputs=[x, s, bias, mean, var],
+            outputs=[y, output_mean, output_var],
+            name="test_batchnorm_example_training_mode",
+        )
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        mean = np.random.randn(3).astype(np.float32)
+        var = np.random.rand(3).astype(np.float32)
+        training_mode = 1
+        momentum = 0.9
+        epsilon = 1e-2
+        y, output_mean, output_var = _batchnorm_training_mode(
+            x, s, bias, mean, var, momentum, epsilon
+        )
+
+        node = onnx.helper.make_node(
+            "BatchNormalization",
+            inputs=["x", "s", "bias", "mean", "var"],
+            outputs=["y", "output_mean", "output_var"],
+            epsilon=epsilon,
+            training_mode=training_mode,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(
+            node,
+            inputs=[x, s, bias, mean, var],
+            outputs=[y, output_mean, output_var],
+            name="test_batchnorm_epsilon_training_mode",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bernoulli.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bernoulli.py
new file mode 100644
index 0000000000000000000000000000000000000000..b52e51682e644493635f90a120cc7c1e4225faed
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bernoulli.py
@@ -0,0 +1,59 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def bernoulli_reference_implementation(x, dtype):
+    # binomial n = 1 equal bernoulli
+    # This example and test-case is for informational purpose. The generator operator is
+    # non-deterministic and may not produce the same values in different implementations
+    # even if a seed is specified.
+    return np.random.binomial(1, p=x).astype(dtype)
+
+
+class Bernoulli(Base):
+    @staticmethod
+    def export_bernoulli_without_dtype() -> None:
+        node = onnx.helper.make_node(
+            "Bernoulli",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(float)
+        y = bernoulli_reference_implementation(x, float)
+        expect(node, inputs=[x], outputs=[y], name="test_bernoulli")
+
+    @staticmethod
+    def export_bernoulli_with_dtype() -> None:
+        node = onnx.helper.make_node(
+            "Bernoulli",
+            inputs=["x"],
+            outputs=["y"],
+            dtype=onnx.TensorProto.DOUBLE,
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y = bernoulli_reference_implementation(x, float)
+        expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double")
+
+    @staticmethod
+    def export_bernoulli_with_seed() -> None:
+        seed = float(0)
+        node = onnx.helper.make_node(
+            "Bernoulli",
+            inputs=["x"],
+            outputs=["y"],
+            seed=seed,
+        )
+
+        x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y = bernoulli_reference_implementation(x, np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitshift.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitshift.py
new file mode 100644
index 0000000000000000000000000000000000000000..372007394feda15c90abadaccdcfdde6666bfc0c
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitshift.py
@@ -0,0 +1,100 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class BitShift(Base):
+    @staticmethod
+    def export_right_unit8() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint8)
+        y = np.array([1, 2, 3]).astype(np.uint8)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint8")
+
+    @staticmethod
+    def export_right_unit16() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint16)
+        y = np.array([1, 2, 3]).astype(np.uint16)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint16")
+
+    @staticmethod
+    def export_right_unit32() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint32)
+        y = np.array([1, 2, 3]).astype(np.uint32)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint32")
+
+    @staticmethod
+    def export_right_unit64() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="RIGHT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint64)
+        y = np.array([1, 2, 3]).astype(np.uint64)
+        z = x >> y  # expected output [8, 1, 0]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_right_uint64")
+
+    @staticmethod
+    def export_left_unit8() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint8)
+        y = np.array([1, 2, 3]).astype(np.uint8)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint8")
+
+    @staticmethod
+    def export_left_unit16() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint16)
+        y = np.array([1, 2, 3]).astype(np.uint16)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint16")
+
+    @staticmethod
+    def export_left_unit32() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint32)
+        y = np.array([1, 2, 3]).astype(np.uint32)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint32")
+
+    @staticmethod
+    def export_left_unit64() -> None:
+        node = onnx.helper.make_node(
+            "BitShift", inputs=["x", "y"], outputs=["z"], direction="LEFT"
+        )
+
+        x = np.array([16, 4, 1]).astype(np.uint64)
+        y = np.array([1, 2, 3]).astype(np.uint64)
+        z = x << y  # expected output [32, 16, 8]
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitshift_left_uint64")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseand.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseand.py
new file mode 100644
index 0000000000000000000000000000000000000000..271ad9e280c00701abfa7177c633a9b21bf9aaf5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseand.py
@@ -0,0 +1,55 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.numpy_helper import create_random_int
+
+
+class BitwiseAnd(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseAnd",
+            inputs=["x", "y"],
+            outputs=["bitwiseand"],
+        )
+
+        # 2d
+        x = create_random_int((3, 4), np.int32)
+        y = create_random_int((3, 4), np.int32)
+        z = np.bitwise_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d")
+
+        # 3d
+        x = create_random_int((3, 4, 5), np.int16)
+        y = create_random_int((3, 4, 5), np.int16)
+        z = np.bitwise_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d")
+
+    @staticmethod
+    def export_bitwiseand_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseAnd",
+            inputs=["x", "y"],
+            outputs=["bitwiseand"],
+        )
+
+        # 3d vs 1d
+        x = create_random_int((3, 4, 5), np.uint64)
+        y = create_random_int((5,), np.uint64)
+        z = np.bitwise_and(x, y)
+        expect(
+            node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d"
+        )
+
+        # 4d vs 3d
+        x = create_random_int((3, 4, 5, 6), np.uint8)
+        y = create_random_int((4, 5, 6), np.uint8)
+        z = np.bitwise_and(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisenot.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisenot.py
new file mode 100644
index 0000000000000000000000000000000000000000..28375830c40ffdf404066a121fead84639c5fe35
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisenot.py
@@ -0,0 +1,36 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.numpy_helper import create_random_int
+
+
+class BitwiseNot(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseNot",
+            inputs=["x"],
+            outputs=["bitwise_not"],
+        )
+
+        # 2d
+        x = create_random_int((3, 4), np.int32)
+        y = np.bitwise_not(x)
+        expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_2d")
+
+        # 3d
+        x = create_random_int((3, 4, 5), np.uint16)
+        y = np.bitwise_not(x)
+        expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_3d")
+
+        # 4d
+        x = create_random_int((3, 4, 5, 6), np.uint8)
+        y = np.bitwise_not(x)
+        expect(node, inputs=[x], outputs=[y], name="test_bitwise_not_4d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseor.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseor.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa1d82acb96d942bdfd945464901d8de2bf0d910
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwiseor.py
@@ -0,0 +1,52 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.numpy_helper import create_random_int
+
+
+class BitwiseOr(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseOr",
+            inputs=["x", "y"],
+            outputs=["bitwiseor"],
+        )
+        # 2d
+        x = create_random_int((3, 4), np.int32)
+        y = create_random_int((3, 4), np.int32)
+        z = np.bitwise_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d")
+
+        # 4d
+        x = create_random_int((3, 4, 5, 6), np.int8)
+        y = create_random_int((3, 4, 5, 6), np.int8)
+        z = np.bitwise_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d")
+
+    @staticmethod
+    def export_bitwiseor_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseOr",
+            inputs=["x", "y"],
+            outputs=["bitwiseor"],
+        )
+
+        # 3d vs 1d
+        x = create_random_int((3, 4, 5), np.uint64)
+        y = create_random_int((5,), np.uint64)
+        z = np.bitwise_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d")
+
+        # 4d vs 3d
+        x = create_random_int((3, 4, 5, 6), np.uint8)
+        y = create_random_int((4, 5, 6), np.uint8)
+        z = np.bitwise_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisexor.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisexor.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbbd98ee5fd9dd548d634da30a5b89f26d547a47
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/bitwisexor.py
@@ -0,0 +1,55 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.numpy_helper import create_random_int
+
+
+class BitwiseXor(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseXor",
+            inputs=["x", "y"],
+            outputs=["bitwisexor"],
+        )
+
+        # 2d
+        x = create_random_int((3, 4), np.int32)
+        y = create_random_int((3, 4), np.int32)
+        z = np.bitwise_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d")
+
+        # 3d
+        x = create_random_int((3, 4, 5), np.int16)
+        y = create_random_int((3, 4, 5), np.int16)
+        z = np.bitwise_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d")
+
+    @staticmethod
+    def export_bitwiseor_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "BitwiseXor",
+            inputs=["x", "y"],
+            outputs=["bitwisexor"],
+        )
+
+        # 3d vs 1d
+        x = create_random_int((3, 4, 5), np.uint64)
+        y = create_random_int((5,), np.uint64)
+        z = np.bitwise_xor(x, y)
+        expect(
+            node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d"
+        )
+
+        # 4d vs 3d
+        x = create_random_int((3, 4, 5, 6), np.uint8)
+        y = create_random_int((4, 5, 6), np.uint8)
+        z = np.bitwise_xor(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/blackmanwindow.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/blackmanwindow.py
new file mode 100644
index 0000000000000000000000000000000000000000..13f92d62da16a071f133cceb819308ed9a20b943
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/blackmanwindow.py
@@ -0,0 +1,56 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class BlackmanWindow(Base):
+    @staticmethod
+    def export() -> None:
+        # Test periodic window
+        node = onnx.helper.make_node(
+            "BlackmanWindow",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        size = np.int32(10)
+        a0 = 0.42
+        a1 = -0.5
+        a2 = 0.08
+        y = a0
+        y += a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size)
+        y += a2 * np.cos(4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size)
+        expect(
+            node,
+            inputs=[size],
+            outputs=[y.astype(np.float32)],
+            name="test_blackmanwindow",
+        )
+
+        # Test symmetric window
+        node = onnx.helper.make_node(
+            "BlackmanWindow", inputs=["x"], outputs=["y"], periodic=0
+        )
+        size = np.int32(10)
+        a0 = 0.42
+        a1 = -0.5
+        a2 = 0.08
+        y = a0
+        y += a1 * np.cos(
+            2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
+        )
+        y += a2 * np.cos(
+            4 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
+        )
+        expect(
+            node,
+            inputs=[size],
+            outputs=[y.astype(np.float32)],
+            name="test_blackmanwindow_symmetric",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/cast.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/cast.py
new file mode 100644
index 0000000000000000000000000000000000000000..621058b95877264f1841edc3ddf035f2e00f7c75
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/cast.py
@@ -0,0 +1,369 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+
+import onnx
+from onnx import TensorProto
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.helper import (
+    make_tensor,
+    tensor_dtype_to_np_dtype,
+)
+from onnx.numpy_helper import to_float8e8m0
+
+F8_TYPES = frozenset({"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"})
+FOUR_BIT_TYPES = frozenset({"UINT4", "INT4", "FLOAT4E2M1"})
+
+
+class Cast(Base):
+    @staticmethod
+    def export() -> None:
+        test_cases = [
+            ("FLOAT", "FLOAT16"),
+            ("FLOAT", "DOUBLE"),
+            ("FLOAT16", "FLOAT"),
+            ("FLOAT16", "DOUBLE"),
+            ("DOUBLE", "FLOAT"),
+            ("DOUBLE", "FLOAT16"),
+            ("FLOAT", "BFLOAT16"),
+            ("BFLOAT16", "FLOAT"),
+            ("FLOAT", "FLOAT8E4M3FN"),
+            ("FLOAT16", "FLOAT8E4M3FN"),
+            ("FLOAT", "FLOAT8E4M3FNUZ"),
+            ("FLOAT16", "FLOAT8E4M3FNUZ"),
+            ("FLOAT8E4M3FN", "FLOAT"),
+            ("FLOAT8E4M3FN", "FLOAT16"),
+            ("FLOAT8E4M3FNUZ", "FLOAT"),
+            ("FLOAT8E4M3FNUZ", "FLOAT16"),
+            ("FLOAT", "FLOAT8E5M2"),
+            ("FLOAT16", "FLOAT8E5M2"),
+            ("FLOAT", "FLOAT8E5M2FNUZ"),
+            ("FLOAT16", "FLOAT8E5M2FNUZ"),
+            ("FLOAT8E5M2", "FLOAT"),
+            ("FLOAT8E5M2", "FLOAT16"),
+            ("FLOAT8E5M2FNUZ", "FLOAT"),
+            ("FLOAT8E5M2FNUZ", "FLOAT16"),
+            ("FLOAT", "UINT4"),
+            ("FLOAT16", "UINT4"),
+            ("FLOAT", "INT4"),
+            ("FLOAT16", "INT4"),
+            ("UINT4", "FLOAT"),
+            ("UINT4", "FLOAT16"),
+            ("UINT4", "UINT8"),
+            ("INT4", "FLOAT"),
+            ("INT4", "FLOAT16"),
+            ("INT4", "INT8"),
+            ("FLOAT4E2M1", "FLOAT"),
+            ("FLOAT4E2M1", "FLOAT16"),
+            ("FLOAT", "FLOAT4E2M1"),
+            ("FLOAT16", "FLOAT4E2M1"),
+        ]
+
+        for from_type, to_type in test_cases:
+            if from_type == to_type:
+                # Skip cases where from_type and to_type are the same
+                continue
+            from_dtype = getattr(TensorProto, from_type)
+            to_dtype = getattr(TensorProto, to_type)
+            from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
+            to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
+
+            if from_type == "BFLOAT16" or to_type == "BFLOAT16":
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.816468",
+                        "0.21087195",
+                        "0.7229038",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 4)
+
+            elif from_type in F8_TYPES or to_type in F8_TYPES:
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.7229038",
+                        "1000000",
+                        "1e-7",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                        "-0.0000001",
+                        "0.0000001",
+                        "-1000000",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 5)
+            elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"):
+                np_fp32 = np.arange(-9, 16).astype(np.float32)
+                input_shape = (5, 5)
+            elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1":
+                np_fp32 = np.array(
+                    [
+                        "0.48",
+                        "0.25",
+                        "1.05",
+                        "-3.5",
+                        "-8",
+                        "9",
+                        "1000000",
+                        "1e-7",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                        "-4",
+                        "0.01",
+                        "-0.0",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 5)
+
+            else:
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.816468",
+                        "0.21087195",
+                        "0.7229038",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                    ],
+                    dtype=np.float32,
+                ).reshape([3, 4])
+                input_shape = (3, 4)
+
+            if from_type in F8_TYPES:
+                np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype)
+                input = make_tensor(
+                    "input",
+                    from_dtype,
+                    input_shape,
+                    vals=np_from,
+                    raw=True,
+                )
+            elif from_type in FOUR_BIT_TYPES:
+                np_from = np_fp32.astype(from_np_dtype)
+                packed = onnx.numpy_helper._pack_4bitx2(np_from)
+                input = make_tensor(
+                    "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True
+                )
+            else:
+                np_from = np_fp32.astype(from_np_dtype)
+                input = make_tensor(
+                    "input", from_dtype, input_shape, vals=np_from, raw=True
+                )
+
+            if to_type in F8_TYPES:
+                output = make_tensor(
+                    "output",
+                    to_dtype,
+                    input_shape,
+                    vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype),
+                    raw=True,
+                )
+            elif to_type in FOUR_BIT_TYPES:
+                packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype))
+                output = make_tensor(
+                    "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True
+                )
+            else:
+                output = make_tensor(
+                    "output",
+                    to_dtype,
+                    input_shape,
+                    vals=np_from.astype(to_np_dtype),
+                    raw=True,
+                )
+
+            node = onnx.helper.make_node(
+                "Cast",
+                inputs=["input"],
+                outputs=["output"],
+                to=to_dtype,
+            )
+            expect(
+                node,
+                inputs=[input],
+                outputs=[output],
+                name="test_cast_" + from_type + "_to_" + to_type,
+            )
+
+    @staticmethod
+    def export_saturate_false() -> None:
+        test_cases = itertools.product(
+            [
+                "FLOAT",
+                "FLOAT16",
+            ],
+            [
+                "FLOAT8E4M3FN",
+                "FLOAT8E4M3FNUZ",
+                "FLOAT8E5M2",
+                "FLOAT8E5M2FNUZ",
+            ],
+        )
+        input_shape = (3, 5)
+        for from_type, to_type in test_cases:
+            from_dtype = getattr(TensorProto, from_type)
+            to_dtype = getattr(TensorProto, to_type)
+            from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
+            to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
+            np_fp32 = np.array(
+                [
+                    "0.47892547",
+                    "0.48033667",
+                    "0.49968487",
+                    "0.81910545",
+                    "0.47031248",
+                    "0.7229038",
+                    "1000000",
+                    "1e-7",
+                    "NaN",
+                    "INF",
+                    "+INF",
+                    "-INF",
+                    "-0.0000001",
+                    "0.0000001",
+                    "-1000000",
+                ],
+                dtype=np.float32,
+            )
+
+            input = make_tensor(
+                "input",
+                from_dtype,
+                input_shape,
+                vals=np_fp32.astype(from_np_dtype),
+                raw=True,
+            )
+            output = make_tensor(
+                "output",
+                to_dtype,
+                input_shape,
+                vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype),
+                raw=True,
+            )
+
+            node = onnx.helper.make_node(
+                "Cast",
+                inputs=["input"],
+                outputs=["output"],
+                to=to_dtype,
+                saturate=0,
+            )
+            expect(
+                node,
+                inputs=[input],
+                outputs=[output],
+                name="test_cast_no_saturate_" + from_type + "_to_" + to_type,
+            )
+
+    @staticmethod
+    def export_e8m0() -> None:
+        np_fp32 = np.array(
+            [
+                "0.0",
+                "0.124",
+                "0.25",
+                "0.5",
+                "1.1",
+                "2.0",
+                "4.0",
+                "8.0",
+            ],
+            dtype=np.float32,
+        )
+        test_cases = [
+            ("FLOAT", "FLOAT8E8M0"),
+            ("FLOAT16", "FLOAT8E8M0"),
+            ("FLOAT8E8M0", "FLOAT"),
+            ("FLOAT8E8M0", "FLOAT16"),
+        ]
+        for from_type, to_type in test_cases:
+            if from_type == "FLOAT":
+                input_np = np_fp32
+                output_np = to_float8e8m0(np_fp32)
+            elif from_type == "FLOAT16":
+                input_np = np_fp32.astype(np.float16)
+                output_np = to_float8e8m0(input_np)
+            elif from_type == "FLOAT8E8M0":
+                input_np = to_float8e8m0(np_fp32)
+                if to_type == "FLOAT":
+                    output_np = input_np.astype(np.float32)
+                elif to_type == "FLOAT16":
+                    output_np = input_np.astype(np.float16)
+                else:
+                    raise ValueError(
+                        f"Conversion from {from_type} to {to_type} is not tested."
+                    )
+            else:
+                raise ValueError(
+                    f"Conversion from {from_type} to {to_type} is not tested."
+                )
+            input = make_tensor(
+                "input",
+                getattr(TensorProto, from_type),
+                [2, 4],
+                input_np.tobytes(),
+                raw=True,
+            )
+            output = make_tensor(
+                "output",
+                getattr(TensorProto, to_type),
+                [2, 4],
+                output_np.tobytes(),
+                raw=True,
+            )
+            if to_type == "FLOAT8E8M0":
+                node = onnx.helper.make_node(
+                    "Cast",
+                    inputs=["input"],
+                    outputs=["output"],
+                    to=getattr(TensorProto, to_type),
+                    saturate=1,
+                    round_mode="up",
+                )
+            else:
+                node = onnx.helper.make_node(
+                    "Cast",
+                    inputs=["input"],
+                    outputs=["output"],
+                    to=getattr(TensorProto, to_type),
+                )
+
+            expect(
+                node,
+                inputs=[input],
+                outputs=[output],
+                name="test_cast_e8m0_" + from_type + "_to_" + to_type,
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/castlike.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/castlike.py
new file mode 100644
index 0000000000000000000000000000000000000000..749c8304b240a3fdf8459703f57f322160b350b3
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/castlike.py
@@ -0,0 +1,291 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+
+import onnx
+from onnx import TensorProto
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.helper import make_tensor, tensor_dtype_to_np_dtype
+
+F8_TYPES = frozenset({"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"})
+FOUR_BIT_TYPES = frozenset({"UINT4", "INT4", "FLOAT4E2M1"})
+
+
+class CastLike(Base):
+    @staticmethod
+    def export() -> None:
+        test_cases = [
+            ("FLOAT", "FLOAT16"),
+            ("FLOAT", "DOUBLE"),
+            ("FLOAT16", "FLOAT"),
+            ("FLOAT16", "DOUBLE"),
+            ("DOUBLE", "FLOAT"),
+            ("DOUBLE", "FLOAT16"),
+            ("FLOAT", "BFLOAT16"),
+            ("BFLOAT16", "FLOAT"),
+            ("FLOAT", "FLOAT8E4M3FN"),
+            ("FLOAT16", "FLOAT8E4M3FN"),
+            ("FLOAT", "FLOAT8E4M3FNUZ"),
+            ("FLOAT16", "FLOAT8E4M3FNUZ"),
+            ("FLOAT8E4M3FN", "FLOAT"),
+            ("FLOAT8E4M3FN", "FLOAT16"),
+            ("FLOAT8E4M3FNUZ", "FLOAT"),
+            ("FLOAT8E4M3FNUZ", "FLOAT16"),
+            ("FLOAT", "FLOAT8E5M2"),
+            ("FLOAT16", "FLOAT8E5M2"),
+            ("FLOAT", "FLOAT8E5M2FNUZ"),
+            ("FLOAT16", "FLOAT8E5M2FNUZ"),
+            ("FLOAT8E5M2", "FLOAT"),
+            ("FLOAT8E5M2", "FLOAT16"),
+            ("FLOAT8E5M2FNUZ", "FLOAT"),
+            ("FLOAT8E5M2FNUZ", "FLOAT16"),
+            ("FLOAT", "UINT4"),
+            ("FLOAT16", "UINT4"),
+            ("FLOAT", "INT4"),
+            ("FLOAT16", "INT4"),
+            ("UINT4", "FLOAT"),
+            ("UINT4", "FLOAT16"),
+            ("UINT4", "UINT8"),
+            ("INT4", "FLOAT"),
+            ("INT4", "FLOAT16"),
+            ("INT4", "INT8"),
+            ("FLOAT4E2M1", "FLOAT"),
+            ("FLOAT4E2M1", "FLOAT16"),
+            ("FLOAT", "FLOAT4E2M1"),
+            ("FLOAT16", "FLOAT4E2M1"),
+        ]
+
+        f8_types = {"FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ"}
+
+        for from_type, to_type in test_cases:
+            if from_type == to_type:
+                # Skip cases where from_type and to_type are the same
+                continue
+            from_dtype = getattr(TensorProto, from_type)
+            to_dtype = getattr(TensorProto, to_type)
+            from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
+            to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
+
+            if from_type == "BFLOAT16" or to_type == "BFLOAT16":
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.816468",
+                        "0.21087195",
+                        "0.7229038",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 4)
+
+            elif from_type in f8_types or to_type in f8_types:
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.7229038",
+                        "1000000",
+                        "1e-7",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                        "-0.0000001",
+                        "0.0000001",
+                        "-1000000",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 5)
+            elif from_type in ("UINT4", "INT4") or to_type in ("UINT4", "INT4"):
+                np_fp32 = np.arange(-9, 16).astype(np.float32)
+                input_shape = (5, 5)
+            elif from_type == "FLOAT4E2M1" or to_type == "FLOAT4E2M1":
+                np_fp32 = np.array(
+                    [
+                        "0.48",
+                        "0.25",
+                        "1.05",
+                        "-3.5",
+                        "-8",
+                        "9",
+                        "1000000",
+                        "1e-7",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                        "-4",
+                        "0.01",
+                        "-0.0",
+                    ],
+                    dtype=np.float32,
+                )
+                input_shape = (3, 5)
+
+            else:
+                np_fp32 = np.array(
+                    [
+                        "0.47892547",
+                        "0.48033667",
+                        "0.49968487",
+                        "0.81910545",
+                        "0.47031248",
+                        "0.816468",
+                        "0.21087195",
+                        "0.7229038",
+                        "NaN",
+                        "INF",
+                        "+INF",
+                        "-INF",
+                    ],
+                    dtype=np.float32,
+                ).reshape([3, 4])
+                input_shape = (3, 4)
+
+            if from_type in F8_TYPES:
+                np_from = onnx.numpy_helper.saturate_cast(np_fp32, from_np_dtype)
+                input = make_tensor(
+                    "input",
+                    from_dtype,
+                    input_shape,
+                    vals=np_from,
+                    raw=True,
+                )
+            elif from_type in FOUR_BIT_TYPES:
+                np_from = np_fp32.astype(from_np_dtype)
+                packed = onnx.numpy_helper._pack_4bitx2(np_from)
+                input = make_tensor(
+                    "input", from_dtype, input_shape, vals=packed.tobytes(), raw=True
+                )
+            else:
+                np_from = np_fp32.astype(from_np_dtype)
+                input = make_tensor(
+                    "input", from_dtype, input_shape, vals=np_from, raw=True
+                )
+
+            if to_type in F8_TYPES:
+                output = make_tensor(
+                    "output",
+                    to_dtype,
+                    input_shape,
+                    vals=onnx.numpy_helper.saturate_cast(np_from, to_np_dtype),
+                    raw=True,
+                )
+            elif to_type in FOUR_BIT_TYPES:
+                packed = onnx.numpy_helper._pack_4bitx2(np_from.astype(to_np_dtype))
+                output = make_tensor(
+                    "output", to_dtype, input_shape, vals=packed.tobytes(), raw=True
+                )
+            else:
+                output = make_tensor(
+                    "output",
+                    to_dtype,
+                    input_shape,
+                    vals=np_from.astype(to_np_dtype),
+                    raw=True,
+                )
+
+            like = make_tensor("like", to_dtype, (0,), vals=[])
+
+            node = onnx.helper.make_node(
+                "CastLike",
+                inputs=["input", "like"],
+                outputs=["output"],
+            )
+
+            expect(
+                node,
+                inputs=[input, like],
+                outputs=[output],
+                name="test_castlike_" + from_type + "_to_" + to_type,
+            )
+
+    @staticmethod
+    def export_saturate_false() -> None:
+        test_cases = itertools.product(
+            [
+                "FLOAT",
+                "FLOAT16",
+            ],
+            [
+                "FLOAT8E4M3FN",
+                "FLOAT8E4M3FNUZ",
+                "FLOAT8E5M2",
+                "FLOAT8E5M2FNUZ",
+            ],
+        )
+        input_shape = (3, 5)
+        for from_type, to_type in test_cases:
+            from_dtype = getattr(TensorProto, from_type)
+            to_dtype = getattr(TensorProto, to_type)
+            from_np_dtype = tensor_dtype_to_np_dtype(from_dtype)
+            to_np_dtype = tensor_dtype_to_np_dtype(to_dtype)
+            np_fp32 = np.array(
+                [
+                    "0.47892547",
+                    "0.48033667",
+                    "0.49968487",
+                    "0.81910545",
+                    "0.47031248",
+                    "0.7229038",
+                    "1000000",
+                    "1e-7",
+                    "NaN",
+                    "INF",
+                    "+INF",
+                    "-INF",
+                    "-0.0000001",
+                    "0.0000001",
+                    "-1000000",
+                ],
+                dtype=np.float32,
+            )
+
+            input = make_tensor(
+                "input",
+                from_dtype,
+                input_shape,
+                vals=np_fp32.astype(from_np_dtype),
+                raw=True,
+            )
+            output = make_tensor(
+                "output",
+                to_dtype,
+                input_shape,
+                vals=np_fp32.astype(from_np_dtype).astype(to_np_dtype),
+                raw=True,
+            )
+
+            like = make_tensor("like", to_dtype, (0,), vals=[])
+
+            node = onnx.helper.make_node(
+                "CastLike",
+                inputs=["input", "like"],
+                outputs=["output"],
+                saturate=0,
+            )
+
+            expect(
+                node,
+                inputs=[input, like],
+                outputs=[output],
+                name="test_castlike_no_saturate_" + from_type + "_to_" + to_type,
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ceil.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ceil.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e4e754c1db4bb2c883858ff847564ccb729899a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/ceil.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Ceil(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Ceil",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.5, 1.2]).astype(np.float32)
+        y = np.ceil(x)  # expected output [-1., 2.]
+        expect(node, inputs=[x], outputs=[y], name="test_ceil_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.ceil(x)
+        expect(node, inputs=[x], outputs=[y], name="test_ceil")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/celu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/celu.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d24b67f98030f40de3606cba24fa92641c65876
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/celu.py
@@ -0,0 +1,50 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Celu(Base):
+    @staticmethod
+    def export() -> None:
+        alpha = 2.0
+        node = onnx.helper.make_node(
+            "Celu",
+            inputs=["X"],
+            outputs=["Y"],
+            alpha=alpha,
+        )
+
+        input_data = np.array(
+            [
+                [
+                    [[0.8439683], [0.5665144], [0.05836735]],
+                    [[0.02916367], [0.12964272], [0.5060197]],
+                    [[0.79538304], [0.9411346], [0.9546573]],
+                ],
+                [
+                    [[0.17730942], [0.46192095], [0.26480448]],
+                    [[0.6746842], [0.01665257], [0.62473077]],
+                    [[0.9240844], [0.9722341], [0.11965699]],
+                ],
+                [
+                    [[0.41356155], [0.9129373], [0.59330076]],
+                    [[0.81929934], [0.7862604], [0.11799799]],
+                    [[0.69248444], [0.54119414], [0.07513223]],
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        # Calculate expected output data
+        positive_input = np.maximum(0, input_data)
+        negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
+        expected_output = positive_input + negative_input
+
+        expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/center_crop_pad.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/center_crop_pad.py
new file mode 100644
index 0000000000000000000000000000000000000000..732192249492f331e94e3244ad2d1c8dc39a2d5e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/center_crop_pad.py
@@ -0,0 +1,130 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class CenterCropPad(Base):
+    @staticmethod
+    def export_center_crop_pad_crop() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+        )
+
+        # First dim is even diff, second is uneven
+        x = np.random.randn(20, 10, 3).astype(np.float32)
+        shape = np.array([10, 7, 3], dtype=np.int64)
+        y = x[5:15, 1:8, :]
+
+        expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop")
+
+    @staticmethod
+    def export_center_crop_pad_pad() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+        )
+
+        # First dim is even diff, second is uneven
+        x = np.random.randn(10, 7, 3).astype(np.float32)
+        shape = np.array([20, 10, 3], dtype=np.int64)
+        y = np.zeros([20, 10, 3], dtype=np.float32)
+        y[5:15, 1:8, :] = x
+
+        expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad")
+
+    @staticmethod
+    def export_center_crop_pad_crop_and_pad() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+        )
+
+        # Cropping on first dim, padding on second, third stays the same
+        x = np.random.randn(20, 8, 3).astype(np.float32)
+        shape = np.array([10, 10, 3], dtype=np.int64)
+        y = np.zeros([10, 10, 3], dtype=np.float32)
+        y[:, 1:9, :] = x[5:15, :, :]
+
+        expect(
+            node,
+            inputs=[x, shape],
+            outputs=[y],
+            name="test_center_crop_pad_crop_and_pad",
+        )
+
+    @staticmethod
+    def export_center_crop_pad_crop_axes_hwc() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+            axes=[0, 1],
+        )
+
+        # Cropping on first dim, padding on second, third stays the same
+        x = np.random.randn(20, 8, 3).astype(np.float32)
+        shape = np.array([10, 9], dtype=np.int64)
+        y = np.zeros([10, 9, 3], dtype=np.float32)
+        y[:, :8, :] = x[5:15, :, :]
+
+        expect(
+            node,
+            inputs=[x, shape],
+            outputs=[y],
+            name="test_center_crop_pad_crop_axes_hwc",
+        )
+
+    @staticmethod
+    def export_center_crop_pad_crop_negative_axes_hwc() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+            axes=[-3, -2],
+        )
+
+        # Cropping on first dim, padding on second, third stays the same
+        x = np.random.randn(20, 8, 3).astype(np.float32)
+        shape = np.array([10, 9], dtype=np.int64)
+        y = np.zeros([10, 9, 3], dtype=np.float32)
+        y[:, :8, :] = x[5:15, :, :]
+
+        expect(
+            node,
+            inputs=[x, shape],
+            outputs=[y],
+            name="test_center_crop_pad_crop_negative_axes_hwc",
+        )
+
+    @staticmethod
+    def export_center_crop_pad_crop_axes_chw() -> None:
+        node = onnx.helper.make_node(
+            "CenterCropPad",
+            inputs=["x", "shape"],
+            outputs=["y"],
+            axes=[1, 2],
+        )
+
+        # Cropping on second dim, padding on third, first stays the same
+        x = np.random.randn(3, 20, 8).astype(np.float32)
+        shape = np.array([10, 9], dtype=np.int64)
+        y = np.zeros([3, 10, 9], dtype=np.float32)
+        y[:, :, :8] = x[:, 5:15, :]
+
+        expect(
+            node,
+            inputs=[x, shape],
+            outputs=[y],
+            name="test_center_crop_pad_crop_axes_chw",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/clip.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c5ec6a59fda3e51e3e34a6f61aa3543a1ec00a9
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/clip.py
@@ -0,0 +1,144 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Clip(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", "min", "max"],
+            outputs=["y"],
+        )
+
+        x = np.array([-2, 0, 2]).astype(np.float32)
+        min_val = np.float32(-1)
+        max_val = np.float32(1)
+        y = np.clip(x, min_val, max_val)  # expected output [-1., 0., 1.]
+        expect(
+            node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_example"
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, min_val, max_val)
+        expect(node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip")
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", "min", "max"],
+            outputs=["y"],
+        )
+
+        min_val = np.float32(-5)
+        max_val = np.float32(5)
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-1, 0, 1]).astype(np.float32)
+        expect(
+            node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_inbounds"
+        )
+
+        x = np.array([-6, 0, 6]).astype(np.float32)
+        y = np.array([-5, 0, 5]).astype(np.float32)
+        expect(
+            node, inputs=[x, min_val, max_val], outputs=[y], name="test_clip_outbounds"
+        )
+
+        x = np.array([-1, 0, 6]).astype(np.float32)
+        y = np.array([-1, 0, 5]).astype(np.float32)
+        expect(
+            node,
+            inputs=[x, min_val, max_val],
+            outputs=[y],
+            name="test_clip_splitbounds",
+        )
+
+        x = np.array([-2, 0, 6]).astype(np.float32)
+        y = np.array([1, 1, 1]).astype(np.float32)
+        min_val = np.float32(2)
+        max_val = np.float32(1)
+        expect(
+            node,
+            inputs=[x, min_val, max_val],
+            outputs=[y],
+            name="test_clip_min_greater_than_max",
+        )
+
+    @staticmethod
+    def export_clip_default() -> None:
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", "min"],
+            outputs=["y"],
+        )
+        min_val = np.float32(0)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, min_val, np.inf)
+        expect(node, inputs=[x, min_val], outputs=[y], name="test_clip_default_min")
+
+        no_min = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", no_min, "max"],
+            outputs=["y"],
+        )
+        max_val = np.float32(0)
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, -np.inf, max_val)
+        expect(node, inputs=[x, max_val], outputs=[y], name="test_clip_default_max")
+
+        no_max = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", no_min, no_max],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-1, 0, 1]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_clip_default_inbounds")
+
+    @staticmethod
+    def export_clip_default_int8() -> None:
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", "min"],
+            outputs=["y"],
+        )
+        min_val = np.int8(0)
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.clip(x, min_val, np.iinfo(np.int8).max)
+        expect(
+            node, inputs=[x, min_val], outputs=[y], name="test_clip_default_int8_min"
+        )
+
+        no_min = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", no_min, "max"],
+            outputs=["y"],
+        )
+        max_val = np.int8(0)
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.clip(x, np.iinfo(np.int8).min, max_val)
+        expect(
+            node, inputs=[x, max_val], outputs=[y], name="test_clip_default_int8_max"
+        )
+
+        no_max = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "Clip",
+            inputs=["x", no_min, no_max],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.int8)
+        y = np.array([-1, 0, 1]).astype(np.int8)
+        expect(node, inputs=[x], outputs=[y], name="test_clip_default_int8_inbounds")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/elu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/elu.py
new file mode 100644
index 0000000000000000000000000000000000000000..d70d9a4be5bd05671844fa378c8f70684bb99f42
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/elu.py
@@ -0,0 +1,37 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Elu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node("Elu", inputs=["x"], outputs=["y"], alpha=2.0)
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-1.2642411, 0., 1.]
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+        expect(node, inputs=[x], outputs=[y], name="test_elu_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0
+        expect(node, inputs=[x], outputs=[y], name="test_elu")
+
+    @staticmethod
+    def export_elu_default() -> None:
+        default_alpha = 1.0
+        node = onnx.helper.make_node(
+            "Elu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha
+        expect(node, inputs=[x], outputs=[y], name="test_elu_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/equal.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/equal.py
new file mode 100644
index 0000000000000000000000000000000000000000..af04a02320d3b3d0dc38c2f6fd68b369fb48239e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/equal.py
@@ -0,0 +1,92 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Equal(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Equal",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        y = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal")
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int8)
+        y = (np.random.randn(3, 4, 5) * 10).astype(np.int8)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_int8")
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int16)
+        y = (np.random.randn(3, 4, 5) * 10).astype(np.int16)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_uint64")
+
+    @staticmethod
+    def export_equal_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Equal",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = (np.random.randn(3, 4, 5) * 10).astype(np.int32)
+        y = (np.random.randn(5) * 10).astype(np.int32)
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_bcast")
+
+    @staticmethod
+    def export_equal_string() -> None:
+        node = onnx.helper.make_node(
+            "Equal",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+        x = np.array(["string1", "string2"], dtype=np.dtype(object))
+        y = np.array(["string1", "string3"], dtype=np.dtype(object))
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_string")
+
+    @staticmethod
+    def export_equal_string_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Equal",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+        x = np.array(["string1", "string2"], dtype=np.dtype(object))
+        y = np.array(["string1"], dtype=np.dtype(object))
+        z = np.equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_equal_string_broadcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/erf.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/erf.py
new file mode 100644
index 0000000000000000000000000000000000000000..807e3779ae3b8b2c371a230fc5c8dc9e69c77206
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/erf.py
@@ -0,0 +1,26 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Erf(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Erf",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        y = np.vectorize(math.erf)(x).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_erf")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/exp.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/exp.py
new file mode 100644
index 0000000000000000000000000000000000000000..d172ff64ac96e9e1b8f1dff15a63691b89a5df94
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/exp.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Exp(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Exp",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.exp(x)  # expected output [0.36787945, 1., 2.71828175]
+        expect(node, inputs=[x], outputs=[y], name="test_exp_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.exp(x)
+        expect(node, inputs=[x], outputs=[y], name="test_exp")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/expand.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/expand.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8637c153146c58d7108910eca6adc6278738bf2
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/expand.py
@@ -0,0 +1,66 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Expand(Base):
+    @staticmethod
+    def export_dim_changed() -> None:
+        node = onnx.helper.make_node(
+            "Expand",
+            inputs=["data", "new_shape"],
+            outputs=["expanded"],
+        )
+        shape = [3, 1]
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[1.], [2.], [3.]]
+        new_shape = [2, 1, 6]
+        expanded = data * np.ones(new_shape, dtype=np.float32)
+        # print(expanded)
+        # [[[1., 1., 1., 1., 1., 1.],
+        #  [2., 2., 2., 2., 2., 2.],
+        #  [3., 3., 3., 3., 3., 3.]],
+        #
+        # [[1., 1., 1., 1., 1., 1.],
+        #  [2., 2., 2., 2., 2., 2.],
+        #  [3., 3., 3., 3., 3., 3.]]]
+        new_shape = np.array(new_shape, dtype=np.int64)
+        expect(
+            node,
+            inputs=[data, new_shape],
+            outputs=[expanded],
+            name="test_expand_dim_changed",
+        )
+
+    @staticmethod
+    def export_dim_unchanged() -> None:
+        node = onnx.helper.make_node(
+            "Expand",
+            inputs=["data", "new_shape"],
+            outputs=["expanded"],
+        )
+        shape = [3, 1]
+        new_shape = [3, 4]
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[1.], [2.], [3.]]
+        expanded = np.tile(data, 4)
+        # print(expanded)
+        # [[1., 1., 1., 1.],
+        # [2., 2., 2., 2.],
+        # [3., 3., 3., 3.]]
+        new_shape = np.array(new_shape, dtype=np.int64)
+        expect(
+            node,
+            inputs=[data, new_shape],
+            outputs=[expanded],
+            name="test_expand_dim_unchanged",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/eyelike.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/eyelike.py
new file mode 100644
index 0000000000000000000000000000000000000000..df25e7eb59372b8815d6ce4df56b5e7c096a6e71
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/eyelike.py
@@ -0,0 +1,60 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class EyeLike(Base):
+    @staticmethod
+    def export_without_dtype() -> None:
+        shape = (4, 4)
+        node = onnx.helper.make_node(
+            "EyeLike",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], dtype=np.int32)
+        expect(node, inputs=[x], outputs=[y], name="test_eyelike_without_dtype")
+
+    @staticmethod
+    def export_with_dtype() -> None:
+        shape = (3, 4)
+        node = onnx.helper.make_node(
+            "EyeLike",
+            inputs=["x"],
+            outputs=["y"],
+            dtype=onnx.TensorProto.DOUBLE,
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], dtype=np.float64)
+        expect(node, inputs=[x], outputs=[y], name="test_eyelike_with_dtype")
+
+    @staticmethod
+    def export_populate_off_main_diagonal() -> None:
+        shape = (4, 5)
+        off_diagonal_offset = 1
+        node = onnx.helper.make_node(
+            "EyeLike",
+            inputs=["x"],
+            outputs=["y"],
+            k=off_diagonal_offset,
+            dtype=onnx.TensorProto.FLOAT,
+        )
+
+        x = np.random.randint(0, 100, size=shape, dtype=np.int32)
+        y = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=np.float32)
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_eyelike_populate_off_main_diagonal",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/flatten.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/flatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec57ca488e2aa1d048a4b4f8c3920e3a2e7ad537
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/flatten.py
@@ -0,0 +1,65 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Flatten(Base):
+    @staticmethod
+    def export() -> None:
+        shape = (2, 3, 4, 5)
+        a = np.random.random_sample(shape).astype(np.float32)
+
+        for i in range(len(shape)):
+            node = onnx.helper.make_node(
+                "Flatten",
+                inputs=["a"],
+                outputs=["b"],
+                axis=i,
+            )
+
+            new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1)
+            b = np.reshape(a, new_shape)
+            expect(node, inputs=[a], outputs=[b], name="test_flatten_axis" + str(i))
+
+    @staticmethod
+    def export_flatten_with_default_axis() -> None:
+        node = onnx.helper.make_node(
+            "Flatten",
+            inputs=["a"],
+            outputs=["b"],  # Default value for axis: axis=1
+        )
+
+        shape = (5, 4, 3, 2)
+        a = np.random.random_sample(shape).astype(np.float32)
+        new_shape = (5, 24)
+        b = np.reshape(a, new_shape)
+        expect(node, inputs=[a], outputs=[b], name="test_flatten_default_axis")
+
+    @staticmethod
+    def export_flatten_negative_axis() -> None:
+        shape = (2, 3, 4, 5)
+        a = np.random.random_sample(shape).astype(np.float32)
+
+        for i in range(-len(shape), 0):
+            node = onnx.helper.make_node(
+                "Flatten",
+                inputs=["a"],
+                outputs=["b"],
+                axis=i,
+            )
+
+            new_shape = (np.prod(shape[0:i]).astype(int), -1)
+            b = np.reshape(a, new_shape)
+            expect(
+                node,
+                inputs=[a],
+                outputs=[b],
+                name="test_flatten_negative_axis" + str(abs(i)),
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/floor.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/floor.py
new file mode 100644
index 0000000000000000000000000000000000000000..c142d1c39a7920abf79aad7d35626301f4db82e5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/floor.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Floor(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Floor",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.5, 1.2, 2]).astype(np.float32)
+        y = np.floor(x)  # expected output [-2., 1., 2.]
+        expect(node, inputs=[x], outputs=[y], name="test_floor_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.floor(x)
+        expect(node, inputs=[x], outputs=[y], name="test_floor")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gather.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gather.py
new file mode 100644
index 0000000000000000000000000000000000000000..37aa464f92950d61ef9e3a5546d0d48865852a40
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gather.py
@@ -0,0 +1,91 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Gather(Base):
+    @staticmethod
+    def export_gather_0() -> None:
+        node = onnx.helper.make_node(
+            "Gather",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=0,
+        )
+        data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+        indices = np.array([0, 1, 3])
+        y = np.take(data, indices, axis=0)
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_0",
+        )
+
+    @staticmethod
+    def export_gather_1() -> None:
+        node = onnx.helper.make_node(
+            "Gather",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=1,
+        )
+        data = np.random.randn(5, 4, 3, 2).astype(np.float32)
+        indices = np.array([0, 1, 3])
+        y = np.take(data, indices, axis=1)
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_1",
+        )
+
+    @staticmethod
+    def export_gather_2d_indices() -> None:
+        node = onnx.helper.make_node(
+            "Gather",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=1,
+        )
+        data = np.random.randn(3, 3).astype(np.float32)
+        indices = np.array([[0, 2]])
+        y = np.take(data, indices, axis=1)
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_2d_indices",
+        )
+
+    @staticmethod
+    def export_gather_negative_indices() -> None:
+        node = onnx.helper.make_node(
+            "Gather",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=0,
+        )
+        data = np.arange(10).astype(np.float32)
+        indices = np.array([0, -9, -10])
+        y = np.take(data, indices, axis=0)
+
+        # print(y)
+        # [0. 1. 0.]
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_negative_indices",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gatherelements.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gatherelements.py
new file mode 100644
index 0000000000000000000000000000000000000000..db4893e7278618b11aababb9467b55d802e3748f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gatherelements.py
@@ -0,0 +1,93 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# The below GatherElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def gather_elements(data, indices, axis=0):
+    data_swaped = np.swapaxes(data, 0, axis)
+    index_swaped = np.swapaxes(indices, 0, axis)
+    gathered = np.choose(index_swaped, data_swaped, mode="wrap")
+    y = np.swapaxes(gathered, 0, axis)
+    return y
+
+
+class GatherElements(Base):
+    @staticmethod
+    def export_gather_elements_0() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "GatherElements",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        indices = np.array([[0, 0], [1, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[1, 1],
+        #  [4, 3]]
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_elements_0",
+        )
+
+    @staticmethod
+    def export_gather_elements_1() -> None:
+        axis = 0
+        node = onnx.helper.make_node(
+            "GatherElements",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
+        indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[4, 8, 3],
+        #  [7, 2, 3]]
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_elements_1",
+        )
+
+    @staticmethod
+    def export_gather_elements_negative_indices() -> None:
+        axis = 0
+        node = onnx.helper.make_node(
+            "GatherElements",
+            inputs=["data", "indices"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
+        indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32)
+
+        y = gather_elements(data, indices, axis)
+        # print(y) produces
+        # [[7, 5, 3],
+        #  [4, 2, 3]]
+
+        expect(
+            node,
+            inputs=[data, indices.astype(np.int64)],
+            outputs=[y],
+            name="test_gather_elements_negative_indices",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gathernd.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gathernd.py
new file mode 100644
index 0000000000000000000000000000000000000000..49abf129723d22414053d802a89950c927086a86
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gathernd.py
@@ -0,0 +1,121 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def gather_nd_impl(
+    data: np.ndarray, indices: np.ndarray, batch_dims: int
+) -> np.ndarray:
+    # Note the data rank - will be reused multiple times later
+    data_rank = len(data.shape)
+
+    # Check input tensors' shape/rank condition
+    assert indices.shape[-1] <= data_rank
+
+    # The list of data/indice shape of batch_dims
+    batch_dims_shape = []
+
+    # The number of elements in the batch_dims for data/indice array
+    batch_dims_size = 1
+
+    # Check the shape of indice and data are identical for batch dims.
+    for i in range(batch_dims):
+        batch_dims_shape.append(indices.shape[i])
+        batch_dims_size *= indices.shape[i]
+
+    # Compute output of the op as below
+
+    # Compute shape of output array
+    output_shape = (
+        batch_dims_shape + list(indices.shape)[batch_dims:-1]
+        if (indices.shape[-1] == data_rank - batch_dims)
+        else batch_dims_shape
+        + list(indices.shape)[batch_dims:-1]
+        + list(data.shape)[batch_dims + indices.shape[-1] :]
+    )
+
+    # Placeholder for output data
+    output_data_buffer = []
+
+    # Flatten 'indices' to 2D array
+    reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1])
+
+    # Flatten 'data' to array of shape (batch_dim_size, data.shape[batch_dimes:])
+    reshaped_data = data.reshape((batch_dims_size, *data.shape[batch_dims:]))
+
+    # gather each scalar value from 'data'
+    for batch_dim in range(reshaped_indices.shape[0]):
+        for outer_dim in range(reshaped_indices.shape[1]):
+            gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
+            output_data_buffer.append(reshaped_data[(batch_dim, *gather_index)])
+    return np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape)
+
+
+class GatherND(Base):
+    @staticmethod
+    def export_int32() -> None:
+        node = onnx.helper.make_node(
+            "GatherND",
+            inputs=["data", "indices"],
+            outputs=["output"],
+        )
+
+        data = np.array([[0, 1], [2, 3]], dtype=np.int32)
+        indices = np.array([[0, 0], [1, 1]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 0)
+        expected_output = np.array([0, 3], dtype=np.int32)
+        assert np.array_equal(output, expected_output)
+        expect(
+            node,
+            inputs=[data, indices],
+            outputs=[output],
+            name="test_gathernd_example_int32",
+        )
+
+    @staticmethod
+    def export_float32() -> None:
+        node = onnx.helper.make_node(
+            "GatherND",
+            inputs=["data", "indices"],
+            outputs=["output"],
+        )
+
+        data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.float32)
+        indices = np.array([[[0, 1]], [[1, 0]]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 0)
+        expected_output = np.array([[[2, 3]], [[4, 5]]], dtype=np.float32)
+        assert np.array_equal(output, expected_output)
+        expect(
+            node,
+            inputs=[data, indices],
+            outputs=[output],
+            name="test_gathernd_example_float32",
+        )
+
+    @staticmethod
+    def export_int32_batchdim_1() -> None:
+        node = onnx.helper.make_node(
+            "GatherND",
+            inputs=["data", "indices"],
+            outputs=["output"],
+            batch_dims=1,
+        )
+
+        data = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype=np.int32)
+        indices = np.array([[1], [0]], dtype=np.int64)
+        output = gather_nd_impl(data, indices, 1)
+        expected_output = np.array([[2, 3], [4, 5]], dtype=np.int32)
+        assert np.array_equal(output, expected_output)
+        expect(
+            node,
+            inputs=[data, indices],
+            outputs=[output],
+            name="test_gathernd_example_int32_batch_dim1",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gelu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gelu.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3f8b90159400598c5ca3c7e34780a2e7364d8ce
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gelu.py
@@ -0,0 +1,52 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Gelu(Base):
+    @staticmethod
+    def export_gelu_tanh() -> None:
+        node = onnx.helper.make_node(
+            "Gelu", inputs=["x"], outputs=["y"], approximate="tanh"
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-0.158808, 0., 0.841192]
+        y = (
+            0.5
+            * x
+            * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
+        ).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_1")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        # expected output [2.9963627, 3.99993, 4.9999995]
+        y = (
+            0.5
+            * x
+            * (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
+        ).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_2")
+
+    @staticmethod
+    def export_gelu_default() -> None:
+        node = onnx.helper.make_node("Gelu", inputs=["x"], outputs=["y"])
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-0.15865526, 0., 0.84134474]
+        y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_gelu_default_1")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        # expected output [2.99595031, 3.99987331, 4.99999857]
+        y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_gelu_default_2")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gemm.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gemm.py
new file mode 100644
index 0000000000000000000000000000000000000000..287ea5570b9c6feee90dc0d4d88fe85da4ba972c
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gemm.py
@@ -0,0 +1,157 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def gemm_reference_implementation(
+    A: np.ndarray,
+    B: np.ndarray,
+    C: np.ndarray | None = None,
+    alpha: float = 1.0,
+    beta: float = 1.0,
+    transA: int = 0,
+    transB: int = 0,
+) -> np.ndarray:
+    A = A if transA == 0 else A.T
+    B = B if transB == 0 else B.T
+    C = C if C is not None else np.array(0)
+
+    Y = alpha * np.dot(A, B) + beta * C
+
+    return Y.astype(A.dtype)
+
+
+class Gemm(Base):
+    @staticmethod
+    def export_default_zero_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
+        a = np.random.ranf([3, 5]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias")
+
+    @staticmethod
+    def export_default_no_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"])
+        a = np.random.ranf([2, 10]).astype(np.float32)
+        b = np.random.ranf([10, 3]).astype(np.float32)
+        y = gemm_reference_implementation(a, b)
+        expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias")
+
+    @staticmethod
+    def export_default_scalar_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
+        a = np.random.ranf([2, 3]).astype(np.float32)
+        b = np.random.ranf([3, 4]).astype(np.float32)
+        c = np.array(3.14).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(
+            node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias"
+        )
+
+    @staticmethod
+    def export_default_single_elem_vector_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
+        a = np.random.ranf([3, 7]).astype(np.float32)
+        b = np.random.ranf([7, 3]).astype(np.float32)
+        c = np.random.ranf([1]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(
+            node,
+            inputs=[a, b, c],
+            outputs=[y],
+            name="test_gemm_default_single_elem_vector_bias",
+        )
+
+    @staticmethod
+    def export_default_vector_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
+        a = np.random.ranf([2, 7]).astype(np.float32)
+        b = np.random.ranf([7, 4]).astype(np.float32)
+        c = np.random.ranf([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(
+            node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias"
+        )
+
+    @staticmethod
+    def export_default_matrix_bias() -> None:
+        node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
+        a = np.random.ranf([3, 6]).astype(np.float32)
+        b = np.random.ranf([6, 4]).astype(np.float32)
+        c = np.random.ranf([3, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c)
+        expect(
+            node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias"
+        )
+
+    @staticmethod
+    def export_transposeA() -> None:
+        node = onnx.helper.make_node(
+            "Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1
+        )
+        a = np.random.ranf([6, 3]).astype(np.float32)
+        b = np.random.ranf([6, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, transA=1)
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA")
+
+    @staticmethod
+    def export_transposeB() -> None:
+        node = onnx.helper.make_node(
+            "Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1
+        )
+        a = np.random.ranf([3, 6]).astype(np.float32)
+        b = np.random.ranf([4, 6]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, transB=1)
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB")
+
+    @staticmethod
+    def export_alpha() -> None:
+        node = onnx.helper.make_node(
+            "Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5
+        )
+        a = np.random.ranf([3, 5]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.zeros([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, alpha=0.5)
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha")
+
+    @staticmethod
+    def export_beta() -> None:
+        node = onnx.helper.make_node(
+            "Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5
+        )
+        a = np.random.ranf([2, 7]).astype(np.float32)
+        b = np.random.ranf([7, 4]).astype(np.float32)
+        c = np.random.ranf([1, 4]).astype(np.float32)
+        y = gemm_reference_implementation(a, b, c, beta=0.5)
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta")
+
+    @staticmethod
+    def export_all_attributes() -> None:
+        node = onnx.helper.make_node(
+            "Gemm",
+            inputs=["a", "b", "c"],
+            outputs=["y"],
+            alpha=0.25,
+            beta=0.35,
+            transA=1,
+            transB=1,
+        )
+        a = np.random.ranf([4, 3]).astype(np.float32)
+        b = np.random.ranf([5, 4]).astype(np.float32)
+        c = np.random.ranf([1, 5]).astype(np.float32)
+        y = gemm_reference_implementation(
+            a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35
+        )
+        expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalaveragepool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalaveragepool.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fddab4b7d42f40be450c6c6f5d12145cec47fae
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalaveragepool.py
@@ -0,0 +1,44 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class GlobalAveragePool(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "GlobalAveragePool",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+        y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+        expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool")
+
+    @staticmethod
+    def export_globalaveragepool_precomputed() -> None:
+        node = onnx.helper.make_node(
+            "GlobalAveragePool",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3],
+                        [4, 5, 6],
+                        [7, 8, 9],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[5]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalmaxpool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalmaxpool.py
new file mode 100644
index 0000000000000000000000000000000000000000..8af125cb0cb559f7bacfd5d30f73c8b2e01d8a21
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/globalmaxpool.py
@@ -0,0 +1,44 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class GlobalMaxPool(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "GlobalMaxPool",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(1, 3, 5, 5).astype(np.float32)
+        y = np.max(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
+        expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool")
+
+    @staticmethod
+    def export_globalmaxpool_precomputed() -> None:
+        node = onnx.helper.make_node(
+            "GlobalMaxPool",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3],
+                        [4, 5, 6],
+                        [7, 8, 9],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[9]]]]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_globalmaxpool_precomputed")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater.py
new file mode 100644
index 0000000000000000000000000000000000000000..98f16fd51d7301c12806205cabcd8ee3837a7515
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater.py
@@ -0,0 +1,68 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Greater(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Greater",
+            inputs=["x", "y"],
+            outputs=["greater"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater")
+
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.random.randn(3, 4, 5).astype(np.int8)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_int8")
+
+        x = np.random.randn(3, 4, 5).astype(np.int16)
+        y = np.random.randn(3, 4, 5).astype(np.int16)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64")
+
+    @staticmethod
+    def export_greater_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Greater",
+            inputs=["x", "y"],
+            outputs=["greater"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.greater(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater_equal.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater_equal.py
new file mode 100644
index 0000000000000000000000000000000000000000..d76e85f01a71e8eb20bc5bf49add22c8ffc95663
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/greater_equal.py
@@ -0,0 +1,68 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Greater(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "GreaterOrEqual",
+            inputs=["x", "y"],
+            outputs=["greater_equal"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal")
+
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.random.randn(3, 4, 5).astype(np.int8)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int8")
+
+        x = np.random.randn(3, 4, 5).astype(np.int16)
+        y = np.random.randn(3, 4, 5).astype(np.int16)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64")
+
+    @staticmethod
+    def export_greater_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "GreaterOrEqual",
+            inputs=["x", "y"],
+            outputs=["greater_equal"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.greater_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gridsample.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gridsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..522b45386215be94c565c95b0a2edcf5dbe1f5aa
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gridsample.py
@@ -0,0 +1,642 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class GridSample(Base):
+    @staticmethod
+    def export_gridsample() -> None:
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            padding_mode="zeros",
+            align_corners=0,
+        )
+        # X shape, [N, C, H, W] - [1, 1, 4, 4]
+        X = np.array(
+            [
+                [
+                    [
+                        [0.0, 1.0, 2.0, 3.0],
+                        [4.0, 5.0, 6.0, 7.0],
+                        [8.0, 9.0, 10.0, 11.0],
+                        [12.0, 13.0, 14.0, 15.0],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-1.0000, -1.0000],
+                        [-0.6000, -1.0000],
+                        [-0.2000, -1.0000],
+                        [0.2000, -1.0000],
+                        [0.6000, -1.0000],
+                        [1.0000, -1.0000],
+                    ],
+                    [
+                        [-1.0000, -0.6000],
+                        [-0.6000, -0.6000],
+                        [-0.2000, -0.6000],
+                        [0.2000, -0.6000],
+                        [0.6000, -0.6000],
+                        [1.0000, -0.6000],
+                    ],
+                    [
+                        [-1.0000, -0.2000],
+                        [-0.6000, -0.2000],
+                        [-0.2000, -0.2000],
+                        [0.2000, -0.2000],
+                        [0.6000, -0.2000],
+                        [1.0000, -0.2000],
+                    ],
+                    [
+                        [-1.0000, 0.2000],
+                        [-0.6000, 0.2000],
+                        [-0.2000, 0.2000],
+                        [0.2000, 0.2000],
+                        [0.6000, 0.2000],
+                        [1.0000, 0.2000],
+                    ],
+                    [
+                        [-1.0000, 0.6000],
+                        [-0.6000, 0.6000],
+                        [-0.2000, 0.6000],
+                        [0.2000, 0.6000],
+                        [0.6000, 0.6000],
+                        [1.0000, 0.6000],
+                    ],
+                    [
+                        [-1.0000, 1.0000],
+                        [-0.6000, 1.0000],
+                        [-0.2000, 1.0000],
+                        [0.2000, 1.0000],
+                        [0.6000, 1.0000],
+                        [1.0000, 1.0000],
+                    ],
+                ]
+            ],
+            dtype=np.float32,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
+                        [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
+                        [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
+                        [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
+                        [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
+                        [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample")
+
+    @staticmethod
+    def export_gridsample_paddingmode() -> None:
+        # X shape, [N, C, H, W] - [1, 1, 3, 2]
+        X = np.array(
+            [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-10.0000, -10.0000],
+                        [-5.0000, -5.0000],
+                        [-0.2000, -0.2000],
+                        [10.0000, 10.0000],
+                    ],
+                    [
+                        [10.0000, 10.0000],
+                        [-0.2000, -0.2000],
+                        [5.0000, 5.0000],
+                        [10.0000, 10.0000],
+                    ],
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # setting padding_mode = 'zeros'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            padding_mode="zeros",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_zeros = np.array(
+            [[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_zeros],
+            name="test_gridsample_zeros_padding",
+        )
+
+        # setting padding_mode = 'border'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            padding_mode="border",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_border = np.array(
+            [[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_border],
+            name="test_gridsample_border_padding",
+        )
+
+        # setting padding_mode = 'reflection'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            padding_mode="reflection",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_reflection = np.array(
+            [[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_reflection],
+            name="test_gridsample_reflection_padding",
+        )
+
+    @staticmethod
+    def export_gridsample_mode_aligncorners() -> None:
+        # X shape, [N, C, H, W] - [1, 1, 3, 2]
+        X = np.array(
+            [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
+            dtype=np.float32,
+        )
+        # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
+        Grid = np.array(
+            [
+                [
+                    [
+                        [-1.0000, -1.0000],
+                        [-0.5000, -0.5000],
+                        [-0.2000, -0.2000],
+                        [0.0000, 0.0000],
+                    ],
+                    [
+                        [0.0000, 0.0000],
+                        [-0.2000, -0.2000],
+                        [0.5000, 0.5000],
+                        [1.0000, 1.0000],
+                    ],
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # setting mode = 'bilinear', default align_corners = 0
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bilinear],
+            name="test_gridsample_bilinear",
+        )
+
+        # setting mode = 'bilinear', align_corners = 1
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_align_corners = np.array(
+            [[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_align_corners],
+            name="test_gridsample_aligncorners_true",
+        )
+
+        # setting mode = 'nearest'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest"
+        )
+
+        # setting mode = 'bicubic'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="cubic",
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bicubic = np.array(
+            [[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic"
+        )
+
+        # ============================================================================
+        # Additional tests
+        # The reference output tensors were generated using PyTorch 2.0.
+        Grid = np.array(
+            [
+                [
+                    [[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]],
+                    [[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]],
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="nearest",
+            align_corners=0,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_nearest],
+            name="test_gridsample_nearest_align_corners_0_additional_1",
+        )
+
+        # setting mode = 'nearest'
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="nearest",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_nearest],
+            name="test_gridsample_nearest_align_corners_1_additional_1",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            align_corners=0,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bilinear],
+            name="test_gridsample_bilinear_align_corners_0_additional_1",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bilinear],
+            name="test_gridsample_bilinear_align_corners_1_additional_1",
+        )
+
+        # These two new bicubic tests produces slightly higher error ~5e-5
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="cubic",
+            align_corners=0,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bicubic = np.array(
+            [
+                [
+                    [
+                        [-0.173250, 0.284265, 1.923106, 2.568000],
+                        [5.170375, 2.284414, 4.744844, 1.046875],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bicubic],
+            name="test_gridsample_bicubic_align_corners_0_additional_1",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="cubic",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bicubic = np.array(
+            [
+                [
+                    [
+                        [0.304001, 1.128750, 2.266270, 3.144844],
+                        [4.531500, 2.455360, 4.599819, 4.000000],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bicubic],
+            name="test_gridsample_bicubic_align_corners_1_additional_1",
+        )
+
+    @staticmethod
+    def export_volumeetric_gridsample_mode_aligncorners() -> None:
+        X = np.array(
+            [
+                [
+                    [
+                        [[1.0, 2.0], [3.0, 4.0]],
+                        [[5.0, 6.0], [7.0, 8.0]],
+                        [[9.0, 10.0], [11.0, 12.0]],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        Grid = np.array(
+            [
+                [
+                    [
+                        [[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]],
+                        [[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]],
+                        [[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]],
+                        [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]],
+                    ],
+                    [
+                        [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]],
+                        [[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]],
+                        [[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]],
+                        [[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]],
+                    ],
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="nearest",
+            align_corners=0,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [
+                [
+                    [
+                        [[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]],
+                        [[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_nearest],
+            name="test_gridsample_volumetric_nearest_align_corners_0",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="nearest",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_nearest = np.array(
+            [
+                [
+                    [
+                        [[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]],
+                        [[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_nearest],
+            name="test_gridsample_volumetric_nearest_align_corners_1",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            align_corners=0,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [
+                [
+                    [
+                        [
+                            [0.1250, 3.4000],
+                            [2.0000, 0.4500],
+                            [4.7000, 10.9000],
+                            [6.5000, 3.0000],
+                        ],
+                        [
+                            [6.5000, 1.7500],
+                            [4.7000, 3.3000],
+                            [11.0000, 2.5200],
+                            [1.5000, 5.4900],
+                        ],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bilinear],
+            name="test_gridsample_volumetric_bilinear_align_corners_0",
+        )
+
+        node = onnx.helper.make_node(
+            "GridSample",
+            inputs=["X", "Grid"],
+            outputs=["Y"],
+            mode="linear",
+            align_corners=1,
+        )
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
+        Y_bilinear = np.array(
+            [
+                [
+                    [
+                        [
+                            [1.0000, 6.7000],
+                            [3.7500, 2.4000],
+                            [5.4000, 9.3000],
+                            [6.5000, 6.0000],
+                        ],
+                        [
+                            [6.5000, 7.0000],
+                            [5.4000, 6.6000],
+                            [9.2500, 8.4000],
+                            [12.0000, 6.1000],
+                        ],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, Grid],
+            outputs=[Y_bilinear],
+            name="test_gridsample_volumetric_bilinear_align_corners_1",
+        )
+
+    """
+    For someone who want to test by script. Comment it cause github ONNX CI
+    do not have the torch python package.
+    @staticmethod
+    def export_gridsample_torch():  # type: () -> None
+        node = onnx.helper.make_node(
+            'GridSample',
+            inputs=['X', 'Grid'],
+            outputs=['Y'],
+            mode='bilinear',
+            padding_mode='zeros',
+            align_corners=0,
+        )
+
+        # X shape, [N, C, H, W] - [1, 1, 4, 4]
+        # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
+        # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
+        import torch
+        X = torch.arange(3 * 3).view(1, 1, 3, 3).float()
+        d = torch.linspace(-1, 1, 6)
+        meshx, meshy = torch.meshgrid((d, d))
+        grid = torch.stack((meshy, meshx), 2)
+        Grid = grid.unsqueeze(0)
+        Y = torch.nn.functional.grid_sample(X, Grid, mode='bilinear',
+                                            padding_mode='zeros', align_corners=False)
+        expect(node, inputs=[X.numpy(), Grid.numpy()], outputs=[Y.numpy()],
+               name='test_gridsample_torch')
+    """
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/groupnormalization.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/groupnormalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..fce3f198217e06778404badc1d9f6235a12777f5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/groupnormalization.py
@@ -0,0 +1,78 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# Group normalization's reference implementation
+def _group_normalization(x, num_groups, scale, bias, epsilon=1e-5):
+    # Assume channel is first dim
+    assert x.shape[1] % num_groups == 0
+    group_size = x.shape[1] // num_groups
+    # Reshape to [N, group_size, C/group_size, H, W, ...]
+    new_shape = [x.shape[0], num_groups, group_size, *list(x.shape[2:])]
+    x_reshaped = x.reshape(new_shape)
+    axes = tuple(range(2, len(new_shape)))
+    mean = np.mean(x_reshaped, axis=axes, keepdims=True)
+    var = np.var(x_reshaped, axis=axes, keepdims=True)
+    x_normalized = ((x_reshaped - mean) / np.sqrt(var + epsilon)).reshape(x.shape)
+    dim_ones = (1,) * (len(x.shape) - 2)
+    scale = scale.reshape(-1, *dim_ones)
+    bias = bias.reshape(-1, *dim_ones)
+    return scale * x_normalized + bias
+
+
+class GroupNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        c = 4
+        num_groups = 2
+        x = np.random.randn(3, c, 2, 2).astype(np.float32)
+        scale = np.random.randn(c).astype(np.float32)
+        bias = np.random.randn(c).astype(np.float32)
+        y = _group_normalization(x, num_groups, scale, bias).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "GroupNormalization",
+            inputs=["x", "scale", "bias"],
+            outputs=["y"],
+            num_groups=num_groups,
+        )
+
+        expect(
+            node,
+            inputs=[x, scale, bias],
+            outputs=[y],
+            name="test_group_normalization_example",
+        )
+
+    @staticmethod
+    def export_epsilon() -> None:
+        c = 4
+        num_groups = 2
+        x = np.random.randn(3, c, 2, 2).astype(np.float32)
+        scale = np.random.randn(c).astype(np.float32)
+        bias = np.random.randn(c).astype(np.float32)
+        epsilon = 1e-2
+        y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "GroupNormalization",
+            inputs=["x", "scale", "bias"],
+            outputs=["y"],
+            epsilon=epsilon,
+            num_groups=num_groups,
+        )
+
+        expect(
+            node,
+            inputs=[x, scale, bias],
+            outputs=[y],
+            name="test_group_normalization_epsilon",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gru.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gru.py
new file mode 100644
index 0000000000000000000000000000000000000000..f205fa27e962479ce48e475d03c625b24deafcb4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/gru.py
@@ -0,0 +1,264 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class GRUHelper:
+    def __init__(self, **params: Any) -> None:
+        # GRU Input Names
+        X = "X"
+        W = "W"
+        R = "R"
+        B = "B"
+        H_0 = "initial_h"
+        LBR = "linear_before_reset"
+        LAYOUT = "layout"
+        number_of_gates = 3
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, f"Missing Required Input: {i}"
+
+        self.num_directions = params[W].shape[0]
+
+        if self.num_directions == 1:
+            for k, v in params.items():
+                if k != X:
+                    params[k] = np.squeeze(v, axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params.get(LAYOUT, 0)
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = (
+                params[B]
+                if B in params
+                else np.zeros(2 * number_of_gates * hidden_size)
+            )
+            h_0 = params[H_0] if H_0 in params else np.zeros((batch_size, hidden_size))
+            lbr = params.get(LBR, 0)
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.H_0 = h_0
+            self.LBR = lbr
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return 1 / (1 + np.exp(-x))
+
+    def g(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        [w_z, w_r, w_h] = np.split(self.W, 3)
+        [r_z, r_r, r_h] = np.split(self.R, 3)
+        [w_bz, w_br, w_bh, r_bz, r_br, r_bh] = np.split(self.B, 6)
+        gates_w = np.transpose(np.concatenate((w_z, w_r)))
+        gates_r = np.transpose(np.concatenate((r_z, r_r)))
+        gates_b = np.add(np.concatenate((w_bz, w_br)), np.concatenate((r_bz, r_br)))
+
+        H_t = self.H_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            gates = np.dot(x, gates_w) + np.dot(H_t, gates_r) + gates_b
+            z, r = np.split(gates, 2, -1)
+            z = self.f(z)
+            r = self.f(r)
+            h_default = self.g(
+                np.dot(x, np.transpose(w_h))
+                + np.dot(r * H_t, np.transpose(r_h))
+                + w_bh
+                + r_bh
+            )
+            h_linear = self.g(
+                np.dot(x, np.transpose(w_h))
+                + r * (np.dot(H_t, np.transpose(r_h)) + r_bh)
+                + w_bh
+            )
+            h = h_linear if self.LBR else h_default
+            H = (1 - z) * h + z * H_t
+            h_list.append(H)
+            H_t = H
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class GRU(Base):
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 5
+        weight_scale = 0.1
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            "GRU", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        gru = GRUHelper(X=input, W=W, R=R)
+        _, Y_h = gru.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_gru_defaults",
+        )
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
+            np.float32
+        )
+
+        input_size = 3
+        hidden_size = 3
+        weight_scale = 0.1
+        custom_bias = 0.1
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            "GRU",
+            inputs=["X", "W", "R", "B"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
+            np.float32
+        )
+        R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        gru = GRUHelper(X=input, W=W, R=R, B=B)
+        _, Y_h = gru.step()
+        expect(
+            node,
+            inputs=[input, W, R, B],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_gru_with_initial_bias",
+        )
+
+    @staticmethod
+    def export_seq_length() -> None:
+        input = np.array(
+            [
+                [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+                [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
+            ]
+        ).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 5
+        number_of_gates = 3
+
+        node = onnx.helper.make_node(
+            "GRU",
+            inputs=["X", "W", "R", "B"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        W = np.random.randn(1, number_of_gates * hidden_size, input_size).astype(
+            np.float32
+        )
+        R = np.random.randn(1, number_of_gates * hidden_size, hidden_size).astype(
+            np.float32
+        )
+
+        # Adding custom bias
+        W_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+        R_B = np.random.randn(1, number_of_gates * hidden_size).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        gru = GRUHelper(X=input, W=W, R=R, B=B)
+        _, Y_h = gru.step()
+        expect(
+            node,
+            inputs=[input, W, R, B],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_gru_seq_length",
+        )
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 6
+        number_of_gates = 3
+        weight_scale = 0.2
+        layout = 1
+
+        node = onnx.helper.make_node(
+            "GRU",
+            inputs=["X", "W", "R"],
+            outputs=["Y", "Y_h"],
+            hidden_size=hidden_size,
+            layout=layout,
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        gru = GRUHelper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = gru.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
+            name="test_gru_batchwise",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hammingwindow.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hammingwindow.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa2c1c6c1d641df4d617c246544bba2524f6ae9d
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hammingwindow.py
@@ -0,0 +1,48 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class HammingWindow(Base):
+    @staticmethod
+    def export() -> None:
+        # Test periodic window
+        node = onnx.helper.make_node(
+            "HammingWindow",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        size = np.int32(10)
+        a0 = 25 / 46
+        a1 = 1 - a0
+        y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size)
+        expect(
+            node,
+            inputs=[size],
+            outputs=[y.astype(np.float32)],
+            name="test_hammingwindow",
+        )
+
+        # Test symmetric window
+        node = onnx.helper.make_node(
+            "HammingWindow", inputs=["x"], outputs=["y"], periodic=0
+        )
+        size = np.int32(10)
+        a0 = 25 / 46
+        a1 = 1 - a0
+        y = a0 - a1 * np.cos(
+            2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
+        )
+        expect(
+            node,
+            inputs=[size],
+            outputs=[y.astype(np.float32)],
+            name="test_hammingwindow_symmetric",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hannwindow.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hannwindow.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ff403c77687b04bf77aacf9382f26f39337bb6
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hannwindow.py
@@ -0,0 +1,45 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class HannWindow(Base):
+    @staticmethod
+    def export() -> None:
+        # Test periodic window
+        node = onnx.helper.make_node(
+            "HannWindow",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        size = np.int32(10)
+        a0 = 0.5
+        a1 = 0.5
+        y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size)
+        expect(
+            node, inputs=[size], outputs=[y.astype(np.float32)], name="test_hannwindow"
+        )
+
+        # Test symmetric window
+        node = onnx.helper.make_node(
+            "HannWindow", inputs=["x"], outputs=["y"], periodic=0
+        )
+        size = np.int32(10)
+        a0 = 0.5
+        a1 = 0.5
+        y = a0 - a1 * np.cos(
+            2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
+        )
+        expect(
+            node,
+            inputs=[size],
+            outputs=[y.astype(np.float32)],
+            name="test_hannwindow_symmetric",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardmax.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..76a31b0ef36343733c1e6815c6f06b0ebb010bec
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardmax.py
@@ -0,0 +1,92 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def hardmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_argmax = np.argmax(x, axis=axis)
+    y = np.zeros_like(x)
+    np.put_along_axis(y, np.expand_dims(x_argmax, axis=axis), 1, axis=axis)
+    return y
+
+
+class Hardmax(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([[3, 0, 1, 2], [2, 5, 1, 0], [0, 1, 3, 2], [0, 1, 2, 3]]).astype(
+            np.float32
+        )
+        # expect result:
+        # [[1. 0. 0. 0.]
+        # [0. 1. 0. 0.]
+        # [0. 0. 1. 0.]
+        # [0. 0. 0. 1.]]
+        y = hardmax(x)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_example")
+
+        # For multiple occurrences of the maximal values, the first occurrence is selected for one-hot output
+        x = np.array([[3, 3, 3, 1]]).astype(np.float32)
+        # expect result:
+        # [[1, 0, 0, 0]]
+        y = hardmax(x)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_one_hot")
+
+    @staticmethod
+    def export_hardmax_axis() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=0,
+        )
+        y = hardmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_0")
+
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=1,
+        )
+        y = hardmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_1")
+
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=2,
+        )
+        y = hardmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_axis_2")
+
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=-1,
+        )
+        y = hardmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_negative_axis")
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            "Hardmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_hardmax_default_axis")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardsigmoid.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardsigmoid.py
new file mode 100644
index 0000000000000000000000000000000000000000..8be888b81bd609f461d64e95eb9ebb328360d0d0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardsigmoid.py
@@ -0,0 +1,39 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class HardSigmoid(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "HardSigmoid", inputs=["x"], outputs=["y"], alpha=0.5, beta=0.6
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.clip(x * 0.5 + 0.6, 0, 1)  # expected output [0.1, 0.6, 1.]
+        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x * 0.5 + 0.6, 0, 1)
+        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid")
+
+    @staticmethod
+    def export_hardsigmoid_default() -> None:
+        default_alpha = 0.2
+        default_beta = 0.5
+        node = onnx.helper.make_node(
+            "HardSigmoid",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x * default_alpha + default_beta, 0, 1)
+        expect(node, inputs=[x], outputs=[y], name="test_hardsigmoid_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardswish.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardswish.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c76d40a79cbc45970cb1169705d705fda878203
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/hardswish.py
@@ -0,0 +1,30 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def hardswish(x: np.ndarray) -> np.ndarray:
+    alfa = float(1 / 6)
+    beta = 0.5
+    return x * np.maximum(0, np.minimum(1, alfa * x + beta))
+
+
+class HardSwish(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "HardSwish",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = hardswish(x)
+
+        expect(node, inputs=[x], outputs=[y], name="test_hardswish")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/identity.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/identity.py
new file mode 100644
index 0000000000000000000000000000000000000000..25e76f6fc9606d95d1c03811c054c295f740a985
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/identity.py
@@ -0,0 +1,93 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Identity(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Identity",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(node, inputs=[data], outputs=[data], name="test_identity")
+
+    @staticmethod
+    def export_sequence() -> None:
+        node = onnx.helper.make_node(
+            "Identity",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        data = [
+            np.array(
+                [
+                    [
+                        [
+                            [1, 2],
+                            [3, 4],
+                        ]
+                    ]
+                ],
+                dtype=np.float32,
+            ),
+            np.array(
+                [
+                    [
+                        [
+                            [2, 3],
+                            [1, 5],
+                        ]
+                    ]
+                ],
+                dtype=np.float32,
+            ),
+        ]
+
+        expect(node, inputs=[data], outputs=[data], name="test_identity_sequence")
+
+    @staticmethod
+    def export_identity_opt() -> None:
+        ten_in_tp = onnx.helper.make_tensor_type_proto(
+            onnx.TensorProto.FLOAT, shape=[5]
+        )
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+        opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+
+        identity_node = onnx.helper.make_node(
+            "Identity", inputs=["opt_in"], outputs=["opt_out"]
+        )
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+
+        expect(
+            identity_node,
+            inputs=[x],
+            outputs=[x],
+            name="test_identity_opt",
+            opset_imports=[onnx.helper.make_opsetid("", 16)],
+            input_type_protos=[opt_in_tp],
+            output_type_protos=[opt_in_tp],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/if.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/if.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd64537b9ccd79f2c1f01ec1482a0ad4ca2dd4f7
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/if.py
@@ -0,0 +1,211 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def compute_if_outputs(x, cond):
+    if cond:
+        return []
+    else:
+        return x
+
+
+class If(Base):
+    @staticmethod
+    def export_if() -> None:
+        # Given a bool scalar input cond.
+        # return constant tensor x if cond is True, otherwise return constant tensor y.
+
+        then_out = onnx.helper.make_tensor_value_info(
+            "then_out", onnx.TensorProto.FLOAT, [5]
+        )
+        else_out = onnx.helper.make_tensor_value_info(
+            "else_out", onnx.TensorProto.FLOAT, [5]
+        )
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+        y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
+
+        then_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["then_out"],
+            value=onnx.numpy_helper.from_array(x),
+        )
+
+        else_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["else_out"],
+            value=onnx.numpy_helper.from_array(y),
+        )
+
+        then_body = onnx.helper.make_graph(
+            [then_const_node], "then_body", [], [then_out]
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node], "else_body", [], [else_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            "If",
+            inputs=["cond"],
+            outputs=["res"],
+            then_branch=then_body,
+            else_branch=else_body,
+        )
+
+        cond = np.array(1).astype(bool)
+        res = x if cond else y
+        expect(
+            if_node,
+            inputs=[cond],
+            outputs=[res],
+            name="test_if",
+            opset_imports=[onnx.helper.make_opsetid("", 11)],
+        )
+
+    @staticmethod
+    def export_if_seq() -> None:
+        # Given a bool scalar input cond.
+        # return constant sequence x if cond is True, otherwise return constant sequence y.
+
+        then_out = onnx.helper.make_tensor_sequence_value_info(
+            "then_out", onnx.TensorProto.FLOAT, shape=[5]
+        )
+        else_out = onnx.helper.make_tensor_sequence_value_info(
+            "else_out", onnx.TensorProto.FLOAT, shape=[5]
+        )
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+        y = [np.array([5, 4, 3, 2, 1]).astype(np.float32)]
+
+        then_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["x"],
+            value=onnx.numpy_helper.from_array(x[0]),
+        )
+
+        then_seq_node = onnx.helper.make_node(
+            "SequenceConstruct", inputs=["x"], outputs=["then_out"]
+        )
+
+        else_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["y"],
+            value=onnx.numpy_helper.from_array(y[0]),
+        )
+
+        else_seq_node = onnx.helper.make_node(
+            "SequenceConstruct", inputs=["y"], outputs=["else_out"]
+        )
+
+        then_body = onnx.helper.make_graph(
+            [then_const_node, then_seq_node], "then_body", [], [then_out]
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node, else_seq_node], "else_body", [], [else_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            "If",
+            inputs=["cond"],
+            outputs=["res"],
+            then_branch=then_body,
+            else_branch=else_body,
+        )
+
+        cond = np.array(1).astype(bool)
+        res = x if cond else y
+        expect(
+            if_node,
+            inputs=[cond],
+            outputs=[res],
+            name="test_if_seq",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+    @staticmethod
+    def export_if_optional() -> None:
+        # Given a bool scalar input cond, return an empty optional sequence of
+        # tensor if True, return an optional sequence with value x
+        # (the input optional sequence) otherwise.
+
+        ten_in_tp = onnx.helper.make_tensor_type_proto(
+            onnx.TensorProto.FLOAT, shape=[5]
+        )
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+
+        then_out_tensor_tp = onnx.helper.make_tensor_type_proto(
+            onnx.TensorProto.FLOAT, shape=[5]
+        )
+        then_out_seq_tp = onnx.helper.make_sequence_type_proto(then_out_tensor_tp)
+        then_out_opt_tp = onnx.helper.make_optional_type_proto(then_out_seq_tp)
+        then_out = onnx.helper.make_value_info("optional_empty", then_out_opt_tp)
+
+        else_out_tensor_tp = onnx.helper.make_tensor_type_proto(
+            onnx.TensorProto.FLOAT, shape=[5]
+        )
+        else_out_seq_tp = onnx.helper.make_sequence_type_proto(else_out_tensor_tp)
+        else_out_opt_tp = onnx.helper.make_optional_type_proto(else_out_seq_tp)
+        else_out = onnx.helper.make_value_info("else_opt", else_out_opt_tp)
+
+        x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
+        cond = np.array(0).astype(bool)
+        res = compute_if_outputs(x, cond)
+
+        opt_empty_in = onnx.helper.make_node(
+            "Optional", inputs=[], outputs=["optional_empty"], type=seq_in_tp
+        )
+
+        then_body = onnx.helper.make_graph([opt_empty_in], "then_body", [], [then_out])
+
+        else_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["x"],
+            value=onnx.numpy_helper.from_array(x[0]),
+        )
+
+        else_seq_node = onnx.helper.make_node(
+            "SequenceConstruct", inputs=["x"], outputs=["else_seq"]
+        )
+
+        else_optional_seq_node = onnx.helper.make_node(
+            "Optional", inputs=["else_seq"], outputs=["else_opt"]
+        )
+
+        else_body = onnx.helper.make_graph(
+            [else_const_node, else_seq_node, else_optional_seq_node],
+            "else_body",
+            [],
+            [else_out],
+        )
+
+        if_node = onnx.helper.make_node(
+            "If",
+            inputs=["cond"],
+            outputs=["sequence"],
+            then_branch=then_body,
+            else_branch=else_body,
+        )
+
+        expect(
+            if_node,
+            inputs=[cond],
+            outputs=[res],
+            name="test_if_opt",
+            output_type_protos=[else_out_opt_tp],
+            opset_imports=[onnx.helper.make_opsetid("", 16)],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/image_decoder.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/image_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b24c3a26a69749d2591df5a7bf7a2f39d0d5056
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/image_decoder.py
@@ -0,0 +1,249 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import io
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import _image_decoder_data, expect
+
+
+def generate_checkerboard(width: int, height: int, square_size: int) -> np.ndarray:
+    # Create an empty RGB image
+    image = np.zeros((height, width, 3), dtype=np.uint8)
+
+    # Calculate the number of squares in each dimension
+    num_squares_x = width // square_size
+    num_squares_y = height // square_size
+
+    # Generate a random color for each square
+    colors = np.random.randint(
+        0, 256, size=(num_squares_y, num_squares_x, 3), dtype=np.uint8
+    )
+
+    # Iterate over each square
+    for i in range(num_squares_y):
+        for j in range(num_squares_x):
+            # Calculate the position of the current square
+            x = j * square_size
+            y = i * square_size
+
+            # Get the color for the current square
+            color = colors[i, j]
+
+            # Fill the square with the corresponding color
+            image[y : y + square_size, x : x + square_size, :] = color
+
+    return image
+
+
+def _generate_test_data(
+    format_: str,
+    frozen_data: _image_decoder_data.ImageDecoderData,
+    pixel_format: str = "RGB",
+    height: int = 32,
+    width: int = 32,
+    tile_sz: int = 5,
+) -> tuple[np.ndarray, np.ndarray]:
+    try:
+        import PIL.Image  # noqa: PLC0415
+    except ImportError:
+        # Since pillow is not installed to generate test data for the ImageDecoder operator
+        # directly use the frozen data from _image_decoder_data.py.
+        return frozen_data.data, frozen_data.output
+    np.random.seed(12345)
+    image = generate_checkerboard(height, width, tile_sz)
+    image_pil = PIL.Image.fromarray(image)
+    with io.BytesIO() as f:
+        image_pil.save(f, format=format_)
+        data = f.getvalue()
+        data_array = np.frombuffer(data, dtype=np.uint8)
+    if pixel_format == "BGR":
+        output_pil = PIL.Image.open(io.BytesIO(data))
+        output = np.array(output_pil)[:, :, ::-1]
+    elif pixel_format == "RGB":
+        output_pil = PIL.Image.open(io.BytesIO(data))
+        output = np.array(output_pil)
+    elif pixel_format == "Grayscale":
+        output_pil = PIL.Image.open(io.BytesIO(data)).convert("L")
+        output = np.array(output_pil)[:, :, np.newaxis]
+    else:
+        raise ValueError(f"Unsupported pixel format: {pixel_format}")
+    return data_array, output
+
+
+class ImageDecoder(Base):
+    @staticmethod
+    def export_image_decoder_decode_jpeg_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "jpeg", _image_decoder_data.image_decoder_decode_jpeg_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_jpeg_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_jpeg_grayscale() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="Grayscale",
+        )
+
+        data, output = _generate_test_data(
+            "jpeg", _image_decoder_data.image_decoder_decode_jpeg_grayscale, "Grayscale"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_jpeg_grayscale",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_jpeg_bgr() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="BGR",
+        )
+
+        data, output = _generate_test_data(
+            "jpeg", _image_decoder_data.image_decoder_decode_jpeg_bgr, "BGR"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_jpeg_bgr",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_jpeg2k_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "jpeg2000", _image_decoder_data.image_decoder_decode_jpeg2k_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_jpeg2k_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_bmp_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "bmp", _image_decoder_data.image_decoder_decode_bmp_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_bmp_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_png_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "png", _image_decoder_data.image_decoder_decode_png_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_png_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_tiff_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "tiff", _image_decoder_data.image_decoder_decode_tiff_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_tiff_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_webp_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "webp", _image_decoder_data.image_decoder_decode_webp_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_webp_rgb",
+        )
+
+    @staticmethod
+    def export_image_decoder_decode_pnm_rgb() -> None:
+        node = onnx.helper.make_node(
+            "ImageDecoder",
+            inputs=["data"],
+            outputs=["output"],
+            pixel_format="RGB",
+        )
+
+        data, output = _generate_test_data(
+            "ppm", _image_decoder_data.image_decoder_decode_pnm_rgb, "RGB"
+        )
+        expect(
+            node,
+            inputs=[data],
+            outputs=[output],
+            name="test_image_decoder_decode_pnm_rgb",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/instancenorm.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/instancenorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d28098f21109c867087f0f9c3adbc4738668b42
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/instancenorm.py
@@ -0,0 +1,56 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class InstanceNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        def _instancenorm_test_mode(x, s, bias, epsilon=1e-5):  # type: ignore
+            dims_x = len(x.shape)
+            axis = tuple(range(2, dims_x))
+            mean = np.mean(x, axis=axis, keepdims=True)
+            var = np.var(x, axis=axis, keepdims=True)
+            dim_ones = (1,) * (dims_x - 2)
+            s = s.reshape(-1, *dim_ones)
+            bias = bias.reshape(-1, *dim_ones)
+            return s * (x - mean) / np.sqrt(var + epsilon) + bias
+
+        # input size: (1, 2, 1, 3)
+        x = np.array([[[[-1, 0, 1]], [[2, 3, 4]]]]).astype(np.float32)
+        s = np.array([1.0, 1.5]).astype(np.float32)
+        bias = np.array([0, 1]).astype(np.float32)
+        y = _instancenorm_test_mode(x, s, bias).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "InstanceNormalization",
+            inputs=["x", "s", "bias"],
+            outputs=["y"],
+        )
+
+        # output size: (1, 2, 1, 3)
+        expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_example")
+
+        # input size: (2, 3, 4, 5)
+        x = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        s = np.random.randn(3).astype(np.float32)
+        bias = np.random.randn(3).astype(np.float32)
+        epsilon = 1e-2
+        y = _instancenorm_test_mode(x, s, bias, epsilon).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "InstanceNormalization",
+            inputs=["x", "s", "bias"],
+            outputs=["y"],
+            epsilon=epsilon,
+        )
+
+        # output size: (2, 3, 4, 5)
+        expect(node, inputs=[x, s, bias], outputs=[y], name="test_instancenorm_epsilon")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isinf.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isinf.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fdd998142180ae110424e41745148dcf1e67b74
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isinf.py
@@ -0,0 +1,56 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class IsInf(Base):
+    @staticmethod
+    def export_infinity() -> None:
+        node = onnx.helper.make_node(
+            "IsInf",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32)
+        y = np.isinf(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isinf")
+
+    @staticmethod
+    def export_positive_infinity_only() -> None:
+        node = onnx.helper.make_node(
+            "IsInf", inputs=["x"], outputs=["y"], detect_negative=0
+        )
+
+        x = np.array([-1.7, np.nan, np.inf, 3.6, -np.inf, np.inf], dtype=np.float32)
+        y = np.isposinf(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isinf_positive")
+
+    @staticmethod
+    def export_negative_infinity_only() -> None:
+        node = onnx.helper.make_node(
+            "IsInf", inputs=["x"], outputs=["y"], detect_positive=0
+        )
+
+        x = np.array([-1.7, np.nan, np.inf, -3.6, -np.inf, np.inf], dtype=np.float32)
+        y = np.isneginf(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isinf_negative")
+
+    @staticmethod
+    def export_infinity_float16() -> None:
+        node = onnx.helper.make_node(
+            "IsInf",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16)
+        y = np.isinf(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isinf_float16")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isnan.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isnan.py
new file mode 100644
index 0000000000000000000000000000000000000000..74d5dff82e9bb004f78f595ef4b0392ca81bf9e4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/isnan.py
@@ -0,0 +1,36 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class IsNaN(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "IsNaN",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float32)
+        y = np.isnan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isnan")
+
+    @staticmethod
+    def export_float16() -> None:
+        node = onnx.helper.make_node(
+            "IsNaN",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1.2, np.nan, np.inf, 2.8, -np.inf, np.inf], dtype=np.float16)
+        y = np.isnan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_isnan_float16")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/layernormalization.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/layernormalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..35eac8e3c8ff8f99234e059bfd75eb4838df3232
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/layernormalization.py
@@ -0,0 +1,178 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# Layer normalization's reference implementation
+def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5):
+    X_shape = X.shape
+    X_rank = len(X_shape)
+    if axis < 0:
+        # If axis = -1 and rank of X is 4,
+        # the axis is changed to -1 + 4 = 3,
+        # which means the last axis.
+        axis = axis + X_rank
+    unsqueezed_rank = X_rank - axis
+    reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
+
+    # Parameter used to convert N-D tensor layer
+    # normalization to equivalent 2-D matrix operations.
+    row_number = 1
+    col_number = 1
+    for i in range(X_rank):
+        if i < axis:
+            row_number *= X_shape[i]
+        else:
+            col_number *= X_shape[i]
+
+    # After reshaping input tensor X into a matrix,
+    # layer normalization is equivalent to conducting
+    # standardization on each column vector (s.t. each
+    # column has zero mean and unit variance).
+    x_mat = np.reshape(X, (row_number, col_number))
+    # This computes mean for every x_mat's column.
+    x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
+    x_diff = x_mat - x_mean
+    x_squared_diff = x_diff * x_diff
+    # This computes variance for every x_mat's column.
+    variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
+    variance_eps = variance + epsilon
+    std_dev = np.sqrt(variance_eps)
+    inv_std_dev = np.reciprocal(std_dev)
+    # Standardization step. y_mat is zero-mean and unit-variance.
+    y_mat = x_diff * inv_std_dev
+    # Apply affine transform on normalization outcome.
+    # W is linear coefficient while B is bias.
+    Y = np.reshape(y_mat, X_shape) * W + B
+    # Matrix-level operations' outputs should be reshaped
+    # to compensate the initial tensor-to-matrix reshape.
+    X_mean = np.reshape(x_mean, reduction_shape)
+    X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
+
+    return Y, X_mean, X_inv_std_dev
+
+
+def calculate_normalized_shape(X_shape, axis):
+    X_rank = len(X_shape)
+    if axis < 0:
+        axis = axis + X_rank
+    return X_shape[axis:]
+
+
+class LayerNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+
+            node = onnx.helper.make_node(
+                "LayerNormalization",
+                inputs=["X", "W", "B"],
+                outputs=["Y", "Mean", "InvStdDev"],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f"test_layer_normalization_4d_axis_negative_{-axis}"
+            else:
+                name = f"test_layer_normalization_4d_axis{axis}"
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export_default_axis() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        # Default axis in LayerNormalization is -1.
+        normalized_shape = calculate_normalized_shape(X.shape, -1)
+        W = np.random.randn(*normalized_shape).astype(np.float32)
+        B = np.random.randn(*normalized_shape).astype(np.float32)
+        # Axis is default to -1 in the reference implementation.
+        Y, mean, inv_std_dev = _layer_normalization(X, W, B)
+
+        # Not specifying axis attribute means -1.
+        node = onnx.helper.make_node(
+            "LayerNormalization",
+            inputs=["X", "W", "B"],
+            outputs=["Y", "Mean", "InvStdDev"],
+        )
+
+        expect(
+            node,
+            inputs=[X, W, B],
+            outputs=[Y, mean, inv_std_dev],
+            name="test_layer_normalization_default_axis",
+        )
+
+    @staticmethod
+    def export2d() -> None:
+        X = np.random.randn(3, 4).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
+
+            node = onnx.helper.make_node(
+                "LayerNormalization",
+                inputs=["X", "W", "B"],
+                outputs=["Y", "Mean", "InvStdDev"],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f"test_layer_normalization_2d_axis_negative_{-axis}"
+            else:
+                name = f"test_layer_normalization_2d_axis{axis}"
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export3d_epsilon() -> None:
+        epsilon = 1e-1
+        X = np.random.randn(2, 3, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon)
+            node = onnx.helper.make_node(
+                "LayerNormalization",
+                inputs=["X", "W", "B"],
+                outputs=["Y", "Mean", "InvStdDev"],
+                axis=axis,
+                epsilon=epsilon,
+            )
+
+            if axis < 0:
+                name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon"
+            else:
+                name = f"test_layer_normalization_3d_axis{axis}_epsilon"
+
+            expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/leakyrelu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/leakyrelu.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb4d2f11bb0760826061b9d690d94168eca6bdce
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/leakyrelu.py
@@ -0,0 +1,39 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class LeakyRelu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "LeakyRelu", inputs=["x"], outputs=["y"], alpha=0.1
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-0.1, 0., 1.]
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+        expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * 0.1
+        expect(node, inputs=[x], outputs=[y], name="test_leakyrelu")
+
+    @staticmethod
+    def export_leakyrelu_default() -> None:
+        default_alpha = 0.01
+        node = onnx.helper.make_node(
+            "LeakyRelu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * default_alpha
+        expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less.py
new file mode 100644
index 0000000000000000000000000000000000000000..184accb14a200a1c6094b87fa9d3bbdee59dca26
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less.py
@@ -0,0 +1,68 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Less(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Less",
+            inputs=["x", "y"],
+            outputs=["less"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less")
+
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.random.randn(3, 4, 5).astype(np.int8)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_int8")
+
+        x = np.random.randn(3, 4, 5).astype(np.int16)
+        y = np.random.randn(3, 4, 5).astype(np.int16)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64")
+
+    @staticmethod
+    def export_less_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Less",
+            inputs=["x", "y"],
+            outputs=["less"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.less(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less_equal.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less_equal.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e4d35f66272e97cf60f6aa7d694724354806159
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/less_equal.py
@@ -0,0 +1,68 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Less(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "LessOrEqual",
+            inputs=["x", "y"],
+            outputs=["less_equal"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal")
+
+        x = np.random.randn(3, 4, 5).astype(np.int8)
+        y = np.random.randn(3, 4, 5).astype(np.int8)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int8")
+
+        x = np.random.randn(3, 4, 5).astype(np.int16)
+        y = np.random.randn(3, 4, 5).astype(np.int16)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_int16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint8")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint16")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint32")
+
+        x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64")
+
+    @staticmethod
+    def export_less_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "LessOrEqual",
+            inputs=["x", "y"],
+            outputs=["less_equal"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = np.less_equal(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/log.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b4d9544b87fed6d8c902a4398902f14fc803a57
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/log.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Log(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Log",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([1, 10]).astype(np.float32)
+        y = np.log(x)  # expected output [0., 2.30258512]
+        expect(node, inputs=[x], outputs=[y], name="test_log_example")
+
+        x = np.exp(np.random.randn(3, 4, 5).astype(np.float32))
+        y = np.log(x)
+        expect(node, inputs=[x], outputs=[y], name="test_log")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/logsoftmax.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/logsoftmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..efc2e6f9268d2b6a98dd8e8f4745355218fb90a1
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/logsoftmax.py
@@ -0,0 +1,92 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def logsoftmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_max = np.max(x, axis=axis, keepdims=True)
+    tmp = np.exp(x - x_max)
+    s = np.sum(tmp, axis=axis, keepdims=True)
+    return (x - x_max) - np.log(s)
+
+
+class LogSoftmax(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.array([[-1, 0, 1]]).astype(np.float32)
+        # expected output
+        # [[-2.4076061 -1.407606  -0.407606 ]]
+        y = logsoftmax(x)
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1")
+
+    @staticmethod
+    def export_logsoftmax_axis() -> None:
+        x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
+        # expected output
+        # [[-3.4401896  -2.4401896  -1.4401896  -0.44018966]
+        # [-3.4401896  -2.4401896  -1.4401896  -0.44018966]]
+        y = logsoftmax(x)
+
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number")
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=0,
+        )
+        y = logsoftmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0")
+
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=1,
+        )
+        y = logsoftmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1")
+
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=2,
+        )
+        y = logsoftmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2")
+
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=-1,
+        )
+        y = logsoftmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis")
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            "LogSoftmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/loop.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..0616be7d97152957036357dfb464d5068a4e9cb6
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/loop.py
@@ -0,0 +1,459 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def compute_loop_outputs(x, seq, trip_count):
+    for i in range(trip_count):
+        if seq is None:
+            seq = []
+        seq += [x[: int(i + 1)]]
+    return seq
+
+
+class Loop(Base):
+    @staticmethod
+    def export_loop_11() -> None:
+        # Given a tensor x of values [x1, ..., xN], and initial tensor y
+        # sum up its elements using a scan
+        # returning the final state (y+x1+x2+...+xN) as well the scan_output
+        # [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
+
+        y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1])
+        y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1])
+        scan_out = onnx.helper.make_tensor_value_info(
+            "scan_out", onnx.TensorProto.FLOAT, [1]
+        )
+        cond_in = onnx.helper.make_tensor_value_info(
+            "cond_in", onnx.TensorProto.BOOL, []
+        )
+        cond_out = onnx.helper.make_tensor_value_info(
+            "cond_out", onnx.TensorProto.BOOL, []
+        )
+        iter_count = onnx.helper.make_tensor_value_info(
+            "iter_count", onnx.TensorProto.INT64, []
+        )
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+        y = np.array([-2]).astype(np.float32)
+
+        x_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["x"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_x",
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            ),
+        )
+
+        one_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["one"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_one",
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1],
+            ),
+        )
+
+        i_add_node = onnx.helper.make_node(
+            "Add", inputs=["iter_count", "one"], outputs=["end"]
+        )
+
+        start_unsqueeze_node = onnx.helper.make_node(
+            "Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0]
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            "Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0]
+        )
+
+        slice_node = onnx.helper.make_node(
+            "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
+        )
+
+        y_add_node = onnx.helper.make_node(
+            "Add", inputs=["y_in", "slice_out"], outputs=["y_out"]
+        )
+
+        identity_node = onnx.helper.make_node(
+            "Identity", inputs=["cond_in"], outputs=["cond_out"]
+        )
+
+        scan_identity_node = onnx.helper.make_node(
+            "Identity", inputs=["y_out"], outputs=["scan_out"]
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [
+                identity_node,
+                x_const_node,
+                one_const_node,
+                i_add_node,
+                start_unsqueeze_node,
+                end_unsqueeze_node,
+                slice_node,
+                y_add_node,
+                scan_identity_node,
+            ],
+            "loop_body",
+            [iter_count, cond_in, y_in],
+            [cond_out, y_out, scan_out],
+        )
+
+        node = onnx.helper.make_node(
+            "Loop",
+            inputs=["trip_count", "cond", "y"],
+            outputs=["res_y", "res_scan"],
+            body=loop_body,
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        res_y = np.array([13]).astype(np.float32)
+        cond = np.array(1).astype(bool)
+        res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
+        expect(
+            node,
+            inputs=[trip_count, cond, y],
+            outputs=[res_y, res_scan],
+            name="test_loop11",
+            opset_imports=[onnx.helper.make_opsetid("", 11)],
+        )
+
+    @staticmethod
+    def export_loop_13() -> None:
+        # Given a tensor x of values [x1, ..., xN],
+        # Return a sequence of tensors of
+        #   [[x1], [x1, x2], ..., [x1, ..., xN]]
+
+        seq_in = onnx.helper.make_tensor_sequence_value_info(
+            "seq_in", onnx.TensorProto.FLOAT, None
+        )
+        seq_out = onnx.helper.make_tensor_sequence_value_info(
+            "seq_out", onnx.TensorProto.FLOAT, None
+        )
+        cond_in = onnx.helper.make_tensor_value_info(
+            "cond_in", onnx.TensorProto.BOOL, []
+        )
+        cond_out = onnx.helper.make_tensor_value_info(
+            "cond_out", onnx.TensorProto.BOOL, []
+        )
+        iter_count = onnx.helper.make_tensor_value_info(
+            "iter_count", onnx.TensorProto.INT64, []
+        )
+
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+        x_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["x"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_x",
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            ),
+        )
+
+        one_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["one"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_one",
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1],
+            ),
+        )
+
+        zero_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["slice_start"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_zero",
+                data_type=onnx.TensorProto.INT64,
+                dims=(1,),
+                vals=[0],
+            ),
+        )
+
+        axes_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["axes"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_axes",
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[0],
+            ),
+        )
+
+        add_node = onnx.helper.make_node(
+            "Add", inputs=["iter_count", "one"], outputs=["end"]
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
+        )
+
+        slice_node = onnx.helper.make_node(
+            "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
+        )
+
+        insert_node = onnx.helper.make_node(
+            "SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"]
+        )
+
+        identity_node = onnx.helper.make_node(
+            "Identity", inputs=["cond_in"], outputs=["cond_out"]
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [
+                identity_node,
+                x_const_node,
+                one_const_node,
+                zero_const_node,
+                add_node,
+                axes_node,
+                end_unsqueeze_node,
+                slice_node,
+                insert_node,
+            ],
+            "loop_body",
+            [iter_count, cond_in, seq_in],
+            [cond_out, seq_out],
+        )
+
+        node = onnx.helper.make_node(
+            "Loop",
+            inputs=["trip_count", "cond", "seq_empty"],
+            outputs=["seq_res"],
+            body=loop_body,
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        seq_empty: list[Any] = []
+        seq_res = [x[: int(i)] for i in x]
+        cond = np.array(1).astype(bool)
+        expect(
+            node,
+            inputs=[trip_count, cond, seq_empty],
+            outputs=[seq_res],
+            name="test_loop13_seq",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+            input_type_protos=[
+                onnx.helper.make_tensor_type_proto(
+                    onnx.TensorProto.INT64, trip_count.shape
+                ),
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                onnx.helper.make_sequence_type_proto(
+                    onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
+                ),
+            ],
+        )
+
+    @staticmethod
+    def export_loop_16_none() -> None:
+        # Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
+        # Return a concatenated sequence of tensors of
+        #   [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
+
+        ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
+        seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
+        opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
+        opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp)
+        seq_out = onnx.helper.make_tensor_sequence_value_info(
+            "seq_out", onnx.TensorProto.FLOAT, []
+        )
+        cond_in = onnx.helper.make_tensor_value_info(
+            "cond_in", onnx.TensorProto.BOOL, []
+        )
+        cond_out = onnx.helper.make_tensor_value_info(
+            "cond_out", onnx.TensorProto.BOOL, []
+        )
+        iter_count = onnx.helper.make_tensor_value_info(
+            "iter_count", onnx.TensorProto.INT64, []
+        )
+
+        x0 = np.array(0).astype(np.float32)
+        x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
+
+        optional_has_elem_node = onnx.helper.make_node(
+            "OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"]
+        )
+
+        optional_is_none = onnx.helper.make_node(
+            "Not", inputs=["optional_has_elem"], outputs=["optional_is_none"]
+        )
+
+        optional_get_elem = onnx.helper.make_node(
+            "OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"]
+        )
+
+        constant_in = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["constant_in"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0]
+            ),
+        )
+
+        seq_const_in = onnx.helper.make_node(
+            "SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"]
+        )
+
+        then_seq_out = onnx.helper.make_tensor_sequence_value_info(
+            "init_seq_in", onnx.TensorProto.FLOAT, []
+        )
+        then_body = onnx.helper.make_graph(
+            [constant_in, seq_const_in], "then_body", [], [then_seq_out]
+        )
+
+        else_seq_out = onnx.helper.make_tensor_sequence_value_info(
+            "seq_in", onnx.TensorProto.FLOAT, []
+        )
+        else_body = onnx.helper.make_graph(
+            [optional_get_elem], "else_body", [], [else_seq_out]
+        )
+
+        if_node = onnx.helper.make_node(
+            "If",
+            inputs=["optional_is_none"],
+            outputs=["sequence"],
+            then_branch=then_body,
+            else_branch=else_body,
+        )
+
+        x_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["x"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_x",
+                data_type=onnx.TensorProto.FLOAT,
+                dims=x.shape,
+                vals=x.flatten().astype(float),
+            ),
+        )
+
+        one_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["one"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_one",
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[1],
+            ),
+        )
+
+        zero_const_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["slice_start"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_zero",
+                data_type=onnx.TensorProto.INT64,
+                dims=(1,),
+                vals=[0],
+            ),
+        )
+
+        axes_node = onnx.helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=["axes"],
+            value=onnx.helper.make_tensor(
+                name="const_tensor_axes",
+                data_type=onnx.TensorProto.INT64,
+                dims=(),
+                vals=[0],
+            ),
+        )
+
+        add_node = onnx.helper.make_node(
+            "Add", inputs=["iter_count", "one"], outputs=["end"]
+        )
+
+        end_unsqueeze_node = onnx.helper.make_node(
+            "Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
+        )
+
+        slice_node = onnx.helper.make_node(
+            "Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
+        )
+
+        insert_node = onnx.helper.make_node(
+            "SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"]
+        )
+
+        identity_node = onnx.helper.make_node(
+            "Identity", inputs=["cond_in"], outputs=["cond_out"]
+        )
+
+        loop_body = onnx.helper.make_graph(
+            [
+                identity_node,
+                optional_has_elem_node,
+                optional_is_none,
+                if_node,
+                x_const_node,
+                one_const_node,
+                zero_const_node,
+                add_node,
+                axes_node,
+                end_unsqueeze_node,
+                slice_node,
+                insert_node,
+            ],
+            "loop_body",
+            [iter_count, cond_in, opt_in],
+            [cond_out, seq_out],
+        )
+
+        node = onnx.helper.make_node(
+            "Loop",
+            inputs=["trip_count", "cond", "opt_seq"],
+            outputs=["seq_res"],
+            body=loop_body,
+        )
+
+        trip_count = np.array(5).astype(np.int64)
+        cond = np.array(1).astype(bool)
+        seq_res = compute_loop_outputs(x, [x0], trip_count)
+        opt_seq_in: list[Any] = [x0]
+        expect(
+            node,
+            inputs=[trip_count, cond, opt_seq_in],
+            outputs=[seq_res],
+            name="test_loop16_seq_none",
+            opset_imports=[onnx.helper.make_opsetid("", 16)],
+            input_type_protos=[
+                onnx.helper.make_tensor_type_proto(
+                    onnx.TensorProto.INT64, trip_count.shape
+                ),
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
+                opt_in_tp,
+            ],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lpnormalization.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lpnormalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..744c68dc1a2ee1019b158776aa06269d7e9de3e9
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lpnormalization.py
@@ -0,0 +1,79 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class LpNormalization(Base):
+    @staticmethod
+    def export_l2normalization_axis_0() -> None:
+        node = onnx.helper.make_node(
+            "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=2
+        )
+        x = np.array(
+            [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]],
+            dtype=np.float32,
+        )
+        l2_norm_axis_0 = np.sqrt(np.sum(x**2, axis=0, keepdims=True))
+        y = x / l2_norm_axis_0
+        expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_0")
+
+    @staticmethod
+    def export_l2normalization_axis_1() -> None:
+        node = onnx.helper.make_node(
+            "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=2
+        )
+        x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32)
+        l2_norm_axis_1 = np.sqrt(np.sum(x**2, axis=1, keepdims=True))
+        y = x / l2_norm_axis_1
+        expect(node, inputs=[x], outputs=[y], name="test_l2normalization_axis_1")
+
+    @staticmethod
+    def export_l1normalization_axis_0() -> None:
+        node = onnx.helper.make_node(
+            "LpNormalization", inputs=["x"], outputs=["y"], axis=0, p=1
+        )
+        x = np.array([3.0, 4.0], dtype=np.float32)
+        l1_norm_axis_0 = np.sum(abs(x), axis=0, keepdims=True)
+        y = x / l1_norm_axis_0
+        expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_0")
+
+    @staticmethod
+    def export_l1normalization_axis_1() -> None:
+        node = onnx.helper.make_node(
+            "LpNormalization", inputs=["x"], outputs=["y"], axis=1, p=1
+        )
+        x = np.array([[3.0, 4.0], [6.0, 8.0]], dtype=np.float32)
+        l1_norm_axis_1 = np.sum(abs(x), axis=1, keepdims=True)
+        y = x / l1_norm_axis_1
+        expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_1")
+
+    @staticmethod
+    def export_l1normalization_axis_last() -> None:
+        node = onnx.helper.make_node(
+            "LpNormalization", inputs=["x"], outputs=["y"], axis=-1, p=1
+        )
+        x = np.array(
+            [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]],
+            dtype=np.float32,
+        )
+        l1_norm_axis_last = np.sum(abs(x), axis=-1, keepdims=True)
+        y = x / l1_norm_axis_last
+        expect(node, inputs=[x], outputs=[y], name="test_l1normalization_axis_last")
+
+    @staticmethod
+    def export_default() -> None:
+        node = onnx.helper.make_node("LpNormalization", inputs=["x"], outputs=["y"])
+        x = np.array(
+            [[[1.0, 2.0, 2.0], [3.0, 4.0, 0.0]], [[0.0, 5.0, 5.0], [6.0, 8.0, 0.0]]],
+            dtype=np.float32,
+        )
+        lp_norm_default = np.sqrt(np.sum(x**2, axis=-1, keepdims=True))
+        y = x / lp_norm_default
+        expect(node, inputs=[x], outputs=[y], name="test_lpnormalization_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lppool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lppool.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1efbafe818d723c85b24fe45c301a83e7bbfb92
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lppool.py
@@ -0,0 +1,298 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_pool_common import (
+    get_output_shape_auto_pad,
+    get_output_shape_explicit_padding,
+    get_pad_shape,
+    pool,
+)
+
+
+class LpPool(Base):
+    @staticmethod
+    def export_lppool_1d_default() -> None:
+        """input_shape: [1, 3, 32]
+        output_shape: [1, 3, 31]
+        """
+        p = 3
+        kernel_shape = [2]
+        strides = [1]
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=kernel_shape,
+            strides=strides,
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_1d_default")
+
+    @staticmethod
+    def export_lppool_2d_default() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 31, 31]
+        """
+        p = 4
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_default")
+
+    @staticmethod
+    def export_lppool_3d_default() -> None:
+        """input_shape: [1, 3, 32, 32, 32]
+        output_shape: [1, 3, 31, 31, 31]
+        """
+        p = 3
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_3d_default")
+
+    @staticmethod
+    def export_lppool_2d_same_upper() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+        """
+        p = 2
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_UPPER",
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_top = pad_shape[0] // 2
+        pad_bottom = pad_shape[0] - pad_top
+        pad_left = pad_shape[1] // 2
+        pad_right = pad_shape[1] - pad_left
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=0,
+        )
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        y = pool(
+            padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper")
+
+    @staticmethod
+    def export_lppool_2d_same_lower() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+        """
+        p = 4
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_LOWER",
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_bottom = pad_shape[0] // 2
+        pad_top = pad_shape[0] - pad_bottom
+        pad_right = pad_shape[1] // 2
+        pad_left = pad_shape[1] - pad_right
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=0,
+        )
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        y = pool(
+            padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, pads, p=p
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower")
+
+    @staticmethod
+    def export_lppool_2d_pads() -> None:
+        """input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        p = 3
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2],
+            p=p,
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = pad_top = pad_right = pad_left = 2
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        out_shape, extra_pads = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = np.pad(
+            x,
+            (
+                (0, 0),
+                (0, 0),
+                (extra_pads[0], extra_pads[2]),
+                (extra_pads[1], extra_pads[3]),
+            ),
+            mode="constant",
+            constant_values=0,
+        )
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "LPPOOL",
+            pads_required=extra_pads,
+            pads=pads,
+            p=p,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads")
+
+    @staticmethod
+    def export_lppool_2d_strides() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 10, 10]
+        """
+        p = 2
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            strides=[3, 3],
+            p=p,
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = (5, 5)
+        strides = (3, 3)
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_strides")
+
+    @staticmethod
+    def export_lppool_2d_dilations() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        p = 2
+        node = onnx.helper.make_node(
+            "LpPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[1, 1],
+            dilations=[2, 2],
+            p=p,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        y = np.array(
+            [
+                [
+                    [
+                        [14.560219778561036, 16.24807680927192],
+                        [21.633307652783937, 23.49468024894146],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_dilations")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lrn.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lrn.py
new file mode 100644
index 0000000000000000000000000000000000000000..86288231d4a4561a9752dab3d5dda2e4b4a00959
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lrn.py
@@ -0,0 +1,70 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class LRN(Base):
+    @staticmethod
+    def export() -> None:
+        alpha = 0.0002
+        beta = 0.5
+        bias = 2.0
+        nsize = 3
+        node = onnx.helper.make_node(
+            "LRN",
+            inputs=["x"],
+            outputs=["y"],
+            alpha=alpha,
+            beta=beta,
+            bias=bias,
+            size=nsize,
+        )
+        x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+        square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+        for n, c, h, w in np.ndindex(x.shape):
+            square_sum[n, c, h, w] = sum(
+                x[
+                    n,
+                    max(0, c - math.floor((nsize - 1) / 2)) : min(
+                        5, c + math.ceil((nsize - 1) / 2) + 1
+                    ),
+                    h,
+                    w,
+                ]
+                ** 2
+            )
+        y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+        expect(node, inputs=[x], outputs=[y], name="test_lrn")
+
+    @staticmethod
+    def export_default() -> None:
+        alpha = 0.0001
+        beta = 0.75
+        bias = 1.0
+        nsize = 3
+        node = onnx.helper.make_node("LRN", inputs=["x"], outputs=["y"], size=3)
+        x = np.random.randn(5, 5, 5, 5).astype(np.float32)
+        square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+        for n, c, h, w in np.ndindex(x.shape):
+            square_sum[n, c, h, w] = sum(
+                x[
+                    n,
+                    max(0, c - math.floor((nsize - 1) / 2)) : min(
+                        5, c + math.ceil((nsize - 1) / 2) + 1
+                    ),
+                    h,
+                    w,
+                ]
+                ** 2
+            )
+        y = x / ((bias + (alpha / nsize) * square_sum) ** beta)
+        expect(node, inputs=[x], outputs=[y], name="test_lrn_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lstm.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lstm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9bf6fed11e0c9d2a9856f6ea23088160f6ae30d
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/lstm.py
@@ -0,0 +1,278 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class LSTMHelper:
+    def __init__(self, **params: Any) -> None:
+        # LSTM Input Names
+        X = "X"
+        W = "W"
+        R = "R"
+        B = "B"
+        H_0 = "initial_h"
+        C_0 = "initial_c"
+        P = "P"
+        LAYOUT = "layout"
+        number_of_gates = 4
+        number_of_peepholes = 3
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, f"Missing Required Input: {i}"
+
+        self.num_directions = params[W].shape[0]
+
+        if self.num_directions == 1:
+            for k, v in params.items():
+                if k != X:
+                    params[k] = np.squeeze(v, axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params.get(LAYOUT, 0)
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = (
+                params[B]
+                if B in params
+                else np.zeros(2 * number_of_gates * hidden_size, dtype=np.float32)
+            )
+            p = (
+                params[P]
+                if P in params
+                else np.zeros(number_of_peepholes * hidden_size, dtype=np.float32)
+            )
+            h_0 = (
+                params[H_0]
+                if H_0 in params
+                else np.zeros((batch_size, hidden_size), dtype=np.float32)
+            )
+            c_0 = (
+                params[C_0]
+                if C_0 in params
+                else np.zeros((batch_size, hidden_size), dtype=np.float32)
+            )
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.P = p
+            self.H_0 = h_0
+            self.C_0 = c_0
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return 1 / (1 + np.exp(-x))
+
+    def g(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def h(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        [p_i, p_o, p_f] = np.split(self.P, 3)
+        H_t = self.H_0
+        C_t = self.C_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            gates = (
+                np.dot(x, np.transpose(self.W))
+                + np.dot(H_t, np.transpose(self.R))
+                + np.add(*np.split(self.B, 2))
+            )
+            i, o, f, c = np.split(gates, 4, -1)
+            i = self.f(i + p_i * C_t)
+            f = self.f(f + p_f * C_t)
+            c = self.g(c)
+            C = f * C_t + i * c
+            o = self.f(o + p_o * C)
+            H = o * self.h(C)
+            h_list.append(H)
+            H_t = H
+            C_t = C
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class LSTM(Base):
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 3
+        weight_scale = 0.1
+        number_of_gates = 4
+
+        node = onnx.helper.make_node(
+            "LSTM", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        lstm = LSTMHelper(X=input, W=W, R=R)
+        _, Y_h = lstm.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_lstm_defaults",
+        )
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
+            np.float32
+        )
+
+        input_size = 3
+        hidden_size = 4
+        weight_scale = 0.1
+        custom_bias = 0.1
+        number_of_gates = 4
+
+        node = onnx.helper.make_node(
+            "LSTM",
+            inputs=["X", "W", "R", "B"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, number_of_gates * hidden_size)).astype(
+            np.float32
+        )
+        R_B = np.zeros((1, number_of_gates * hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), 1)
+
+        lstm = LSTMHelper(X=input, W=W, R=R, B=B)
+        _, Y_h = lstm.step()
+        expect(
+            node,
+            inputs=[input, W, R, B],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_lstm_with_initial_bias",
+        )
+
+    @staticmethod
+    def export_peepholes() -> None:
+        input = np.array([[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]).astype(
+            np.float32
+        )
+
+        input_size = 4
+        hidden_size = 3
+        weight_scale = 0.1
+        number_of_gates = 4
+        number_of_peepholes = 3
+
+        node = onnx.helper.make_node(
+            "LSTM",
+            inputs=["X", "W", "R", "B", "sequence_lens", "initial_h", "initial_c", "P"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        # Initializing Inputs
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+        B = np.zeros((1, 2 * number_of_gates * hidden_size)).astype(np.float32)
+        seq_lens = np.repeat(input.shape[0], input.shape[1]).astype(np.int32)
+        init_h = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+        init_c = np.zeros((1, input.shape[1], hidden_size)).astype(np.float32)
+        P = weight_scale * np.ones((1, number_of_peepholes * hidden_size)).astype(
+            np.float32
+        )
+
+        lstm = LSTMHelper(
+            X=input, W=W, R=R, B=B, P=P, initial_c=init_c, initial_h=init_h
+        )
+        _, Y_h = lstm.step()
+        expect(
+            node,
+            inputs=[input, W, R, B, seq_lens, init_h, init_c, P],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_lstm_with_peepholes",
+        )
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 7
+        weight_scale = 0.3
+        number_of_gates = 4
+        layout = 1
+
+        node = onnx.helper.make_node(
+            "LSTM",
+            inputs=["X", "W", "R"],
+            outputs=["Y", "Y_h"],
+            hidden_size=hidden_size,
+            layout=layout,
+        )
+
+        W = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, input_size)
+        ).astype(np.float32)
+        R = weight_scale * np.ones(
+            (1, number_of_gates * hidden_size, hidden_size)
+        ).astype(np.float32)
+
+        lstm = LSTMHelper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = lstm.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
+            name="test_lstm_batchwise",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmul.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5be47374f227d4120cff279024dbf5a0a289e34
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmul.py
@@ -0,0 +1,38 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class MatMul(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "MatMul",
+            inputs=["a", "b"],
+            outputs=["c"],
+        )
+
+        # 2d
+        a = np.random.randn(3, 4).astype(np.float32)
+        b = np.random.randn(4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c], name="test_matmul_2d")
+
+        # 3d
+        a = np.random.randn(2, 3, 4).astype(np.float32)
+        b = np.random.randn(2, 4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c], name="test_matmul_3d")
+
+        # 4d
+        a = np.random.randn(1, 2, 3, 4).astype(np.float32)
+        b = np.random.randn(1, 2, 4, 3).astype(np.float32)
+        c = np.matmul(a, b)
+        expect(node, inputs=[a, b], outputs=[c], name="test_matmul_4d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmulinteger.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmulinteger.py
new file mode 100644
index 0000000000000000000000000000000000000000..684a2f1f6f9c3f35fd44ecef22b04e396f35f40e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/matmulinteger.py
@@ -0,0 +1,60 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class MatMulInteger(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "MatMulInteger",
+            inputs=["A", "B", "a_zero_point", "b_zero_point"],
+            outputs=["Y"],
+        )
+
+        A = np.array(
+            [
+                [11, 7, 3],
+                [10, 6, 2],
+                [9, 5, 1],
+                [8, 4, 0],
+            ],
+            dtype=np.uint8,
+        )
+
+        a_zero_point = np.array([12], dtype=np.uint8)
+
+        B = np.array(
+            [
+                [1, 4],
+                [2, 5],
+                [3, 6],
+            ],
+            dtype=np.uint8,
+        )
+
+        b_zero_point = np.array([0], dtype=np.uint8)
+
+        output = np.array(
+            [
+                [-38, -83],
+                [-44, -98],
+                [-50, -113],
+                [-56, -128],
+            ],
+            dtype=np.int32,
+        )
+
+        expect(
+            node,
+            inputs=[A, B, a_zero_point, b_zero_point],
+            outputs=[output],
+            name="test_matmulinteger",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/max.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/max.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f2c9f122899154396afeef6f23c348bc6e7a7df
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/max.py
@@ -0,0 +1,66 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.backend.test.case.utils import all_numeric_dtypes
+
+
+class Max(Base):
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 2, 1]).astype(np.float32)
+        data_1 = np.array([1, 4, 4]).astype(np.float32)
+        data_2 = np.array([2, 5, 3]).astype(np.float32)
+        result = np.array([3, 5, 4]).astype(np.float32)
+        node = onnx.helper.make_node(
+            "Max",
+            inputs=["data_0", "data_1", "data_2"],
+            outputs=["result"],
+        )
+        expect(
+            node,
+            inputs=[data_0, data_1, data_2],
+            outputs=[result],
+            name="test_max_example",
+        )
+
+        node = onnx.helper.make_node(
+            "Max",
+            inputs=["data_0"],
+            outputs=["result"],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input")
+
+        result = np.maximum(data_0, data_1)
+        node = onnx.helper.make_node(
+            "Max",
+            inputs=["data_0", "data_1"],
+            outputs=["result"],
+        )
+        expect(
+            node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs"
+        )
+
+    @staticmethod
+    def export_max_all_numeric_types() -> None:
+        for op_dtype in all_numeric_dtypes:
+            data_0 = np.array([3, 2, 1]).astype(op_dtype)
+            data_1 = np.array([1, 4, 4]).astype(op_dtype)
+            result = np.array([3, 4, 4]).astype(op_dtype)
+            node = onnx.helper.make_node(
+                "Max",
+                inputs=["data_0", "data_1"],
+                outputs=["result"],
+            )
+            expect(
+                node,
+                inputs=[data_0, data_1],
+                outputs=[result],
+                name=f"test_max_{np.dtype(op_dtype).name}",
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxpool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxpool.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeedf72c5680d360791867b0de30f91fa8e58271
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxpool.py
@@ -0,0 +1,725 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_pool_common import (
+    get_output_shape_auto_pad,
+    get_output_shape_explicit_padding,
+    get_pad_shape,
+    pool,
+)
+
+
+class MaxPool(Base):
+    @staticmethod
+    def export_maxpool_2d_uint8() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.uint8)
+        y = np.array(
+            [
+                [
+                    [
+                        [13, 14, 15, 15, 15],
+                        [18, 19, 20, 20, 20],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.uint8)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8")
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_pads() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array(
+            [
+                [
+                    [
+                        [13, 14, 15, 15, 15],
+                        [18, 19, 20, 20, 20],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads")
+
+    @staticmethod
+    def export_maxpool_with_argmax_2d_precomputed_pads() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 5, 5]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y", "z"],
+            kernel_shape=[5, 5],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array(
+            [
+                [
+                    [
+                        [13, 14, 15, 15, 15],
+                        [18, 19, 20, 20, 20],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                        [23, 24, 25, 25, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        z = np.array(
+            [
+                [
+                    [
+                        [12, 13, 14, 14, 14],
+                        [17, 18, 19, 19, 19],
+                        [22, 23, 24, 24, 24],
+                        [22, 23, 24, 24, 24],
+                        [22, 23, 24, 24, 24],
+                    ]
+                ]
+            ]
+        ).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y, z],
+            name="test_maxpool_with_argmax_2d_precomputed_pads",
+        )
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_strides() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[2, 2], strides=[2, 2]
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
+
+        expect(
+            node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides"
+        )
+
+    @staticmethod
+    def export_maxpool_with_argmax_2d_precomputed_strides() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y", "z"],
+            kernel_shape=[2, 2],
+            strides=[2, 2],
+            storage_order=1,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[7, 9], [17, 19]]]]).astype(np.float32)
+        z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y, z],
+            name="test_maxpool_with_argmax_2d_precomputed_strides",
+        )
+
+    @staticmethod
+    def export_maxpool_2d_precomputed_same_upper() -> None:
+        """input_shape: [1, 1, 5, 5]
+        output_shape: [1, 1, 3, 3]
+        pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            auto_pad="SAME_UPPER",
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4, 5],
+                        [6, 7, 8, 9, 10],
+                        [11, 12, 13, 14, 15],
+                        [16, 17, 18, 19, 20],
+                        [21, 22, 23, 24, 25],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32)
+
+        expect(
+            node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper"
+        )
+
+    @staticmethod
+    def export_maxpool_1d_default() -> None:
+        """input_shape: [1, 3, 32]
+        output_shape: [1, 3, 31]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2],
+        )
+        x = np.random.randn(1, 3, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = [2]
+        strides = [1]
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default")
+
+    @staticmethod
+    def export_maxpool_2d_default() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default")
+
+    @staticmethod
+    def export_maxpool_3d_default() -> None:
+        """input_shape: [1, 3, 32, 32, 32]
+        output_shape: [1, 3, 31, 31, 31]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        out_shape, _ = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default")
+
+    @staticmethod
+    def export_maxpool_2d_same_upper() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_UPPER",
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_top = pad_shape[0] // 2
+        pad_bottom = pad_shape[0] - pad_top
+        pad_left = pad_shape[1] // 2
+        pad_right = pad_shape[1] - pad_left
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper")
+
+    @staticmethod
+    def export_maxpool_2d_same_lower() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 32, 32]
+        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            auto_pad="SAME_LOWER",
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (2, 2)
+        strides = (1, 1)
+        out_shape = get_output_shape_auto_pad(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides
+        )
+        pad_shape = get_pad_shape(
+            "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
+        )
+        pad_bottom = pad_shape[0] // 2
+        pad_top = pad_shape[0] - pad_bottom
+        pad_right = pad_shape[1] // 2
+        pad_left = pad_shape[1] - pad_right
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX", pads, pads)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower")
+
+    @staticmethod
+    def export_maxpool_2d_pads() -> None:
+        """input_shape: [1, 3, 28, 28]
+        output_shape: [1, 3, 30, 30]
+        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            pads=[2, 2, 2, 2],
+        )
+        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
+        x_shape = np.shape(x)
+        kernel_shape = (3, 3)
+        strides = (1, 1)
+        pad_bottom = pad_top = pad_right = pad_left = 2
+        pads = [pad_top, pad_left, pad_bottom, pad_right]
+        out_shape, extra_pads = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = np.pad(
+            x,
+            ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
+            mode="constant",
+            constant_values=np.nan,
+        )
+
+        y = pool(
+            padded,
+            x_shape,
+            kernel_shape,
+            strides,
+            out_shape,
+            "MAX",
+            pads_required=extra_pads,
+            pads=pads,
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads")
+
+    @staticmethod
+    def export_maxpool_2d_strides() -> None:
+        """input_shape: [1, 3, 32, 32]
+        output_shape: [1, 3, 10, 10]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool", inputs=["x"], outputs=["y"], kernel_shape=[5, 5], strides=[3, 3]
+        )
+        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
+        x_shape = np.shape(x)
+        pads = None
+        kernel_shape = (5, 5)
+        strides = (3, 3)
+        out_shape, pads = get_output_shape_explicit_padding(
+            pads, x_shape[2:], kernel_shape, strides
+        )
+        padded = x
+        y = pool(padded, x_shape, kernel_shape, strides, out_shape, "MAX")
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides")
+
+    @staticmethod
+    def export_maxpool_2d_ceil() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[3, 3],
+            strides=[2, 2],
+            ceil_mode=True,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil")
+
+    @staticmethod
+    def export_maxpool_2d_ceil_output_size_reduce_by_one() -> None:
+        """input_shape: [1, 1, 2, 2]
+        output_shape: [1, 1, 1, 1]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[1, 1],
+            strides=[2, 2],
+            ceil_mode=True,
+        )
+        x = np.array([[[[1, 2], [3, 4]]]]).astype(np.float32)
+        y = np.array([[[[1]]]]).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_maxpool_2d_ceil_output_size_reduce_by_one",
+        )
+
+    @staticmethod
+    def export_maxpool_2d_dilations() -> None:
+        """input_shape: [1, 1, 4, 4]
+        output_shape: [1, 1, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[1, 1],
+            dilations=[2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[11, 12], [15, 16]]]]).astype(np.float32)
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations")
+
+    @staticmethod
+    def export_maxpool_3d_dilations() -> None:
+        """input_shape: [1, 1, 4, 4, 4]
+        output_shape: [1, 1, 2, 2, 2]
+        """
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+            strides=[1, 1, 1],
+            dilations=[2, 2, 2],
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+        y = np.array([[[[[11, 12], [15, 16]], [[11, 12], [15, 16]]]]]).astype(
+            np.float32
+        )
+
+        expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations")
+
+    @staticmethod
+    def export_maxpool_3d_dilations_use_ref_impl() -> None:
+        """input_shape: [1, 1, 4, 4, 4]
+        output_shape: [1, 1, 2, 2, 2]
+        """
+        dilations = [2, 2, 2]
+        kernel_shape = [2, 2, 2]
+        strides = [1, 1, 1]
+        ceil_mode = False
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=[2, 2, 2],
+            strides=[1, 1, 1],
+            dilations=dilations,
+        )
+        x = np.array(
+            [
+                [
+                    [
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                        [
+                            [1, 2, 3, 4],
+                            [5, 6, 7, 8],
+                            [9, 10, 11, 12],
+                            [13, 14, 15, 16],
+                        ],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        x_shape = x.shape[2:]
+        out_shape, pads = get_output_shape_explicit_padding(
+            None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode
+        )
+        padded = x
+        y = pool(
+            padded,
+            (1, 1, *x_shape),
+            kernel_shape,
+            strides,
+            out_shape,
+            "MAX",
+            pads_required=pads,
+            pads=None,
+            dilations=dilations,
+        )
+
+        expect(
+            node, inputs=[x], outputs=[y], name="test_maxpool_3d_dilations_use_ref_impl"
+        )
+
+    @staticmethod
+    def export_maxpool_3d_dilations_use_ref_impl_large() -> None:
+        x_shape = (32, 32, 32)
+        dilations = (2, 2, 2)
+        kernel_shape = (5, 5, 5)
+        strides = (3, 3, 3)
+        ceil_mode = True
+
+        node = onnx.helper.make_node(
+            "MaxPool",
+            inputs=["x"],
+            outputs=["y"],
+            kernel_shape=kernel_shape,
+            strides=strides,
+            dilations=dilations,
+            ceil_mode=ceil_mode,
+        )
+
+        x = np.random.randn(1, 1, *x_shape).astype(np.float32)
+        out_shape, pads = get_output_shape_explicit_padding(
+            None, x_shape, kernel_shape, strides, dilations, ceil_mode=ceil_mode
+        )
+        padded = np.pad(
+            x,
+            (
+                (0, 0),
+                (0, 0),
+                (pads[0], pads[3]),
+                (pads[1], pads[4]),
+                (pads[2], pads[5]),
+            ),
+            mode="constant",
+            constant_values=0,
+        )
+        y = pool(
+            padded,
+            (1, 1, *x_shape),
+            kernel_shape,
+            strides,
+            out_shape,
+            "MAX",
+            pads_required=pads,
+            pads=None,
+            dilations=dilations,
+        )
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_maxpool_3d_dilations_use_ref_impl_large",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxunpool.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxunpool.py
new file mode 100644
index 0000000000000000000000000000000000000000..65643189a30ec7fea1183cf58fb1b04f78f32d4a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/maxunpool.py
@@ -0,0 +1,67 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class MaxUnpool(Base):
+    @staticmethod
+    def export_without_output_shape() -> None:
+        node = onnx.helper.make_node(
+            "MaxUnpool",
+            inputs=["xT", "xI"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[2, 2],
+        )
+        xT = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
+        xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
+        y = np.array(
+            [[[[0, 0, 0, 0], [0, 1, 0, 2], [0, 0, 0, 0], [0, 3, 0, 4]]]],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[xT, xI],
+            outputs=[y],
+            name="test_maxunpool_export_without_output_shape",
+        )
+
+    @staticmethod
+    def export_with_output_shape() -> None:
+        node = onnx.helper.make_node(
+            "MaxUnpool",
+            inputs=["xT", "xI", "output_shape"],
+            outputs=["y"],
+            kernel_shape=[2, 2],
+            strides=[2, 2],
+        )
+        xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32)
+        xI = np.array([[[[5, 7], [13, 15]]]], dtype=np.int64)
+        output_shape = np.array((1, 1, 5, 5), dtype=np.int64)
+        y = np.array(
+            [
+                [
+                    [
+                        [0, 0, 0, 0, 0],
+                        [0, 5, 0, 6, 0],
+                        [0, 0, 0, 0, 0],
+                        [0, 7, 0, 8, 0],
+                        [0, 0, 0, 0, 0],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[xT, xI, output_shape],
+            outputs=[y],
+            name="test_maxunpool_export_with_output_shape",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mean.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mean.py
new file mode 100644
index 0000000000000000000000000000000000000000..25061bad7e88276c7c869264fc78d9546bf75a15
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mean.py
@@ -0,0 +1,47 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Mean(Base):
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 0, 2]).astype(np.float32)
+        data_1 = np.array([1, 3, 4]).astype(np.float32)
+        data_2 = np.array([2, 6, 6]).astype(np.float32)
+        result = np.array([2, 3, 4]).astype(np.float32)
+        node = onnx.helper.make_node(
+            "Mean",
+            inputs=["data_0", "data_1", "data_2"],
+            outputs=["result"],
+        )
+        expect(
+            node,
+            inputs=[data_0, data_1, data_2],
+            outputs=[result],
+            name="test_mean_example",
+        )
+
+        node = onnx.helper.make_node(
+            "Mean",
+            inputs=["data_0"],
+            outputs=["result"],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0], name="test_mean_one_input")
+
+        result = np.divide(np.add(data_0, data_1), 2.0)
+        node = onnx.helper.make_node(
+            "Mean",
+            inputs=["data_0", "data_1"],
+            outputs=["result"],
+        )
+        expect(
+            node, inputs=[data_0, data_1], outputs=[result], name="test_mean_two_inputs"
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/meanvariancenormalization.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/meanvariancenormalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..241c2d53dafb8660f455560879e8d81354914e82
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/meanvariancenormalization.py
@@ -0,0 +1,49 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class MeanVarianceNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
+        )
+
+        input_data = np.array(
+            [
+                [
+                    [[0.8439683], [0.5665144], [0.05836735]],
+                    [[0.02916367], [0.12964272], [0.5060197]],
+                    [[0.79538304], [0.9411346], [0.9546573]],
+                ],
+                [
+                    [[0.17730942], [0.46192095], [0.26480448]],
+                    [[0.6746842], [0.01665257], [0.62473077]],
+                    [[0.9240844], [0.9722341], [0.11965699]],
+                ],
+                [
+                    [[0.41356155], [0.9129373], [0.59330076]],
+                    [[0.81929934], [0.7862604], [0.11799799]],
+                    [[0.69248444], [0.54119414], [0.07513223]],
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        # Calculate expected output data
+        data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
+        data_mean_squared = np.power(data_mean, 2)
+        data_squared = np.power(input_data, 2)
+        data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
+        std = np.sqrt(data_squared_mean - data_mean_squared)
+        expected_output = (input_data - data_mean) / (std + 1e-9)
+
+        expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/melweightmatrix.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/melweightmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7c73540d4d0ba5b8b9afe27cad0571748fe1860
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/melweightmatrix.py
@@ -0,0 +1,90 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class MelWeightMatrix(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "MelWeightMatrix",
+            inputs=[
+                "num_mel_bins",
+                "dft_length",
+                "sample_rate",
+                "lower_edge_hertz",
+                "upper_edge_hertz",
+            ],
+            outputs=["output"],
+        )
+
+        num_mel_bins = np.int32(8)
+        dft_length = np.int32(16)
+        sample_rate = np.int32(8192)
+        lower_edge_hertz = np.float32(0)
+        upper_edge_hertz = np.float32(8192 / 2)
+
+        num_spectrogram_bins = dft_length // 2 + 1
+        frequency_bins = np.arange(0, num_mel_bins + 2)
+
+        low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
+        high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
+        mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0]
+
+        frequency_bins = frequency_bins * mel_step + low_frequency_mel
+        frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
+        frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate
+        frequency_bins = frequency_bins.astype(int)
+
+        output = np.zeros((num_spectrogram_bins, num_mel_bins))
+        output.flags.writeable = True
+
+        for i in range(num_mel_bins):
+            lower_frequency_value = frequency_bins[i]  # left
+            center_frequency_point = frequency_bins[i + 1]  # center
+            higher_frequency_point = frequency_bins[i + 2]  # right
+            low_to_center = center_frequency_point - lower_frequency_value
+            if low_to_center == 0:
+                output[center_frequency_point, i] = 1
+            else:
+                for j in range(lower_frequency_value, center_frequency_point + 1):
+                    output[j, i] = float(j - lower_frequency_value) / float(
+                        low_to_center
+                    )
+            center_to_high = higher_frequency_point - center_frequency_point
+            if center_to_high > 0:
+                for j in range(center_frequency_point, higher_frequency_point):
+                    output[j, i] = float(higher_frequency_point - j) / float(
+                        center_to_high
+                    )
+
+        # Expected output
+        # 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+        # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+        output = output.astype(np.float32)
+        expect(
+            node,
+            inputs=[
+                num_mel_bins,
+                dft_length,
+                sample_rate,
+                lower_edge_hertz,
+                upper_edge_hertz,
+            ],
+            outputs=[output],
+            name="test_melweightmatrix",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/min.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/min.py
new file mode 100644
index 0000000000000000000000000000000000000000..82d407f07f9efe974fa3396fece988d54799f9e1
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/min.py
@@ -0,0 +1,66 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.backend.test.case.utils import all_numeric_dtypes
+
+
+class Min(Base):
+    @staticmethod
+    def export() -> None:
+        data_0 = np.array([3, 2, 1]).astype(np.float32)
+        data_1 = np.array([1, 4, 4]).astype(np.float32)
+        data_2 = np.array([2, 5, 0]).astype(np.float32)
+        result = np.array([1, 2, 0]).astype(np.float32)
+        node = onnx.helper.make_node(
+            "Min",
+            inputs=["data_0", "data_1", "data_2"],
+            outputs=["result"],
+        )
+        expect(
+            node,
+            inputs=[data_0, data_1, data_2],
+            outputs=[result],
+            name="test_min_example",
+        )
+
+        node = onnx.helper.make_node(
+            "Min",
+            inputs=["data_0"],
+            outputs=["result"],
+        )
+        expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input")
+
+        result = np.minimum(data_0, data_1)
+        node = onnx.helper.make_node(
+            "Min",
+            inputs=["data_0", "data_1"],
+            outputs=["result"],
+        )
+        expect(
+            node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs"
+        )
+
+    @staticmethod
+    def export_min_all_numeric_types() -> None:
+        for op_dtype in all_numeric_dtypes:
+            data_0 = np.array([3, 2, 1]).astype(op_dtype)
+            data_1 = np.array([1, 4, 4]).astype(op_dtype)
+            result = np.array([1, 2, 1]).astype(op_dtype)
+            node = onnx.helper.make_node(
+                "Min",
+                inputs=["data_0", "data_1"],
+                outputs=["result"],
+            )
+            expect(
+                node,
+                inputs=[data_0, data_1],
+                outputs=[result],
+                name=f"test_min_{np.dtype(op_dtype).name}",
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mish.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mish.py
new file mode 100644
index 0000000000000000000000000000000000000000..0359ebb32d34858e0522bb762af8f0c1ab219d7e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mish.py
@@ -0,0 +1,23 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Mish(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"])
+
+        input_data = np.linspace(-10, 10, 10000, dtype=np.float32)
+
+        # Calculate expected output data
+        expected_output = input_data * np.tanh(np.log1p(np.exp(input_data)))
+
+        expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mod.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..c76613a022b5b0bf08fd71955169db96136674ff
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mod.py
@@ -0,0 +1,177 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Mod(Base):
+    @staticmethod
+    def export_mod_mixed_sign_float64() -> None:
+        node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float64)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float64)
+        z = np.fmod(x, y)  # expected output [-0.1,  0.4,  5. ,  0.1, -0.4,  3.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float64")
+
+    @staticmethod
+    def export_mod_mixed_sign_float32() -> None:
+        node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float32)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float32)
+        z = np.fmod(
+            x, y
+        )  # expected output [-0.10000038, 0.39999962, 5. , 0.10000038, -0.39999962, 3.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float32")
+
+    @staticmethod
+    def export_mod_mixed_sign_float16() -> None:
+        node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
+
+        x = np.array([-4.3, 7.2, 5.0, 4.3, -7.2, 8.0]).astype(np.float16)
+        y = np.array([2.1, -3.4, 8.0, -2.1, 3.4, 5.0]).astype(np.float16)
+        z = np.fmod(
+            x, y
+        )  # expected output [-0.10156, 0.3984 , 5. , 0.10156, -0.3984 ,  3.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_float16")
+
+    @staticmethod
+    def export_mod_mixed_sign_int64() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int64")
+
+    @staticmethod
+    def export_mod_mixed_sign_int32() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int32)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int32)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int32")
+
+    @staticmethod
+    def export_mod_mixed_sign_int16() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int16)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int16)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int16")
+
+    @staticmethod
+    def export_mod_mixed_sign_int8() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int8)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int8)
+        z = np.mod(x, y)  # expected output [ 0, -2,  5,  0,  2,  3]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_mixed_sign_int8")
+
+    @staticmethod
+    def export_mod_uint8() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint8)
+        y = np.array([2, 3, 8]).astype(np.uint8)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint8")
+
+    @staticmethod
+    def export_mod_uint16() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint16)
+        y = np.array([2, 3, 8]).astype(np.uint16)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint16")
+
+    @staticmethod
+    def export_mod_uint32() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint32)
+        y = np.array([2, 3, 8]).astype(np.uint32)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint32")
+
+    @staticmethod
+    def export_mod_uint64() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([4, 7, 5]).astype(np.uint64)
+        y = np.array([2, 3, 8]).astype(np.uint64)
+        z = np.mod(x, y)  # expected output [0, 1, 5]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_uint64")
+
+    @staticmethod
+    def export_mod_int64_fmod() -> None:
+        node = onnx.helper.make_node("Mod", inputs=["x", "y"], outputs=["z"], fmod=1)
+
+        x = np.array([-4, 7, 5, 4, -7, 8]).astype(np.int64)
+        y = np.array([2, -3, 8, -2, 3, 5]).astype(np.int64)
+        z = np.fmod(x, y)  # expected output [ 0,  1,  5,  0, -1,  3]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_int64_fmod")
+
+    @staticmethod
+    def export_mod_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Mod",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.arange(0, 30).reshape([3, 2, 5]).astype(np.int32)
+        y = np.array([7]).astype(np.int32)
+        z = np.mod(x, y)
+        #   array([[[0, 1, 2, 3, 4],
+        #     [5, 6, 0, 1, 2]],
+
+        #    [[3, 4, 5, 6, 0],
+        #     [1, 2, 3, 4, 5]],
+
+        #    [[6, 0, 1, 2, 3],
+        #     [4, 5, 6, 0, 1]]], dtype=int32)
+        expect(node, inputs=[x, y], outputs=[z], name="test_mod_broadcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/momentum.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/momentum.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e5c4f822b24d6e9272bc1abba6bc0dadcf9a652
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/momentum.py
@@ -0,0 +1,162 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN
+
+
+def apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta):
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Coefficient of gradient should be 1 at the first iteration.
+    beta_adjusted = beta if t > 0 else 1
+    # Update momentum.
+    v_new = alpha * v + beta_adjusted * g_regularized
+    # Apply SG with momentum update rule.
+    x_new = x - r * v_new
+    return x_new, v_new
+
+
+def apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta):
+    # Add gradient of regularization term.
+    g_regularized = norm_coefficient * x + g
+    # Coefficient of gradient should be 1 at the first iteration.
+    beta_adjusted = beta if t > 0 else 1
+    # Update momentum.
+    v_new = alpha * v + beta_adjusted * g_regularized
+    # Apply Nesterov with momentum update rule.
+    x_new = x - r * (g_regularized + alpha * v_new)
+    return x_new, v_new
+
+
+class Momentum(Base):
+    @staticmethod
+    def export_momentum() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.1
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "Momentum",
+            inputs=["R", "T", "X", "G", "V"],
+            outputs=["X_new", "V_new"],
+            norm_coefficient=norm_coefficient,
+            alpha=alpha,
+            beta=beta,
+            mode="standard",
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x_new, v_new = apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x, g, v],
+            outputs=[x_new, v_new],
+            name="test_momentum",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
+
+    @staticmethod
+    def export_nesterov_momentum() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.01
+        alpha = 0.95
+        beta = 1.0
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "Momentum",
+            inputs=["R", "T", "X", "G", "V"],
+            outputs=["X_new", "V_new"],
+            norm_coefficient=norm_coefficient,
+            alpha=alpha,
+            beta=beta,
+            mode="nesterov",
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+        x = np.array([1.2, 2.8], dtype=np.float32)
+        g = np.array([-0.94, -2.5], dtype=np.float32)
+        v = np.array([1.7, 3.6], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x_new, v_new = apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x, g, v],
+            outputs=[x_new, v_new],
+            name="test_nesterov_momentum",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
+
+    @staticmethod
+    def export_momentum_multiple() -> None:
+        # Define operator attributes.
+        norm_coefficient = 0.001
+        alpha = 0.95
+        beta = 0.85
+
+        node = onnx.helper.make_node(
+            "Momentum",
+            inputs=["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
+            outputs=["X1_new", "X2_new", "V1_new", "V2_new"],
+            norm_coefficient=norm_coefficient,
+            alpha=alpha,
+            beta=beta,
+            mode="standard",
+            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
+        )
+
+        # Define operator inputs.
+        r = np.array(0.1, dtype=np.float32)  # scalar
+        t = np.array(0, dtype=np.int64)  # scalar
+
+        x1 = np.array([1.0], dtype=np.float32)
+        g1 = np.array([-1.0], dtype=np.float32)
+        v1 = np.array([2.0], dtype=np.float32)
+
+        x2 = np.array([1.0, 2.0], dtype=np.float32)
+        g2 = np.array([-1.0, -3.0], dtype=np.float32)
+        v2 = np.array([4.0, 1.0], dtype=np.float32)
+
+        # Compute expected outputs of Momentum.
+        x1_new, v1_new = apply_momentum(r, t, x1, g1, v1, norm_coefficient, alpha, beta)
+        x2_new, v2_new = apply_momentum(r, t, x2, g2, v2, norm_coefficient, alpha, beta)
+
+        # Check results.
+        expect(
+            node,
+            inputs=[r, t, x1, x2, g1, g2, v1, v2],
+            outputs=[x1_new, x2_new, v1_new, v2_new],
+            name="test_momentum_multiple",
+            opset_imports=[
+                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
+            ],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mul.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mul.py
new file mode 100644
index 0000000000000000000000000000000000000000..b279173b0f82c018c7e4c6af308e11de222e51f4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/mul.py
@@ -0,0 +1,73 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Mul(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Mul",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = x * y  # expected output [4., 10., 18.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.int8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.int8)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_int8")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.int16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.int16)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_int16")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint16)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint16)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint16")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint32)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint32)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint32")
+
+        x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint64)
+        y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint64)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint64")
+
+    @staticmethod
+    def export_mul_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Mul",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.random.randn(5).astype(np.float32)
+        z = x * y
+        expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/neg.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/neg.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b86e155b6f97f644e50bc3cf292ff0907f6fdfe
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/neg.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Neg(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Neg",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-4, 2]).astype(np.float32)
+        y = np.negative(x)  # expected output [4., -2.],
+        expect(node, inputs=[x], outputs=[y], name="test_neg_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.negative(x)
+        expect(node, inputs=[x], outputs=[y], name="test_neg")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/negativeloglikelihoodloss.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/negativeloglikelihoodloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..944c69b563b74184f8c8f2e099d2889291140509
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/negativeloglikelihoodloss.py
@@ -0,0 +1,586 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def compute_negative_log_likelihood_loss(
+    input, target, weight=None, reduction="mean", ignore_index=None
+):
+    input_shape = input.shape
+    if len(input_shape) == 1:
+        raise RuntimeError("Unsupported shape")
+
+    target_shape = target.shape
+    N = input_shape[0]
+    C = input_shape[1]
+
+    # initialize the positional weights when required
+    gather_weight = None
+    if weight is not None:
+        # setting mode='clip' to deal with ignore_index > C or < 0 cases.
+        # when the target value is > C or < 0, it doesn't matter which value we are
+        # taking in gather_weight, since it will be set to 0 in the following if-block
+        # use np.int32 to make it compatible with x86 machines
+        gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
+        # set `ignore_index`'s loss weight to 0.
+        # The loss tensor will be multiplied by this weight tensor,
+        # so `ingore_index`'s loss value will be eliminated.
+        if ignore_index is not None:
+            gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
+                dtype=np.float32
+            )
+    elif ignore_index is not None:
+        gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
+
+    # if input is 4-d and above, make it 3-d
+    if len(input_shape) != 3:
+        input = input.reshape((N, C, -1))
+        target = target.reshape((N, -1))
+
+    # Get a dimension from the reshaped input.
+    # If the original input shape is [N, C, H, W],
+    # the D here should be H * W because we reshape
+    # [N, C, H, W] to [N, C, H * W].
+    D = input.shape[2]
+    neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
+    for i in range(N):
+        for d in range(D):
+            if target[i][d] != ignore_index:
+                neg_gather_element_input[i][d] = -input[i][target[i][d]][d]
+
+    loss = neg_gather_element_input
+
+    # if the input was 4-d or above reshape to the right shape
+    if len(input_shape) != 3:
+        loss = loss.reshape(target_shape)
+
+    # apply the weights when required
+    if gather_weight is not None:
+        loss = gather_weight * loss
+        if reduction == "mean":
+            loss = loss.sum() / gather_weight.sum()
+            return loss
+
+    if reduction == "mean":
+        loss = np.mean(loss)
+    elif reduction == "sum":
+        loss = np.sum(loss)
+    return loss
+
+
+class NegativeLogLikelihoodLoss(Base):
+    @staticmethod
+    def export_input_shape_is_NC() -> None:
+        reduction = "none"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C = 3, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N,)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NC",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2() -> None:
+        reduction = "none"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_reduction_mean() -> None:
+        reduction = "mean"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_reduction_mean",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_reduction_sum() -> None:
+        reduction = "sum"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_reduction_sum",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight() -> None:
+        reduction = "none"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_with_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_mean() -> None:
+        reduction = "mean"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_with_weight_reduction_mean",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_sum() -> None:
+        reduction = "sum"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_with_weight_reduction_sum",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_with_weight_reduction_sum_ii() -> None:
+        reduction = "sum"
+        ignore_index = np.int64(0)
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        target[0][0][0] = np.int64(0)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2_no_weight_reduction_mean_ii() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1, dim2 = 3, 5, 6, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
+        target[0][0][0] = np.int64(1)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1() -> None:
+        reduction = "mean"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_weight() -> None:
+        reduction = "mean"
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_ii() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        target[0][0] = np.int64(1)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=None, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_weight_ii() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(1)
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, d1 = 3, 5, 2
+        np.random.seed(0)
+        input = np.random.rand(N, C, d1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
+        target[0][0] = np.int64(1)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1_weight_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
+        reduction = "mean"
+
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        target = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2d3d4d5_mean_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
+        reduction = "none"
+
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        target = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, reduction=reduction
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2d3d4d5_none_no_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        target[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1_mean_weight_negative_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
+        reduction = "none"
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
+            np.int64
+        )
+        target[0][0][0][0] = -5
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
+        reduction = "sum"
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node(
+            "NegativeLogLikelihoodLoss",
+            inputs=["input", "target", "weight"],
+            outputs=["loss"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C = 3, 5
+        np.random.seed(0)
+        input = np.random.rand(N, C).astype(np.float32)
+        target = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+        target[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
+            input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[input, target, weight],
+            outputs=[negative_log_likelihood_loss],
+            name="test_nllloss_NCd1d2d3_sum_weight_high_ii",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonmaxsuppression.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonmaxsuppression.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ab4017835f93f5c7d49ff95eaeef0251e7a310a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonmaxsuppression.py
@@ -0,0 +1,420 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class NonMaxSuppression(Base):
+    @staticmethod
+    def export_nonmaxsuppression_suppress_by_IOU() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_suppress_by_IOU",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_suppress_by_IOU_and_scores() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.4]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_suppress_by_IOU_and_scores",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_flipped_coordinates() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [1.0, 1.0, 0.0, 0.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, 0.9, 1.0, -0.1],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [1.0, 10.1, 0.0, 11.1],
+                    [1.0, 101.0, 0.0, 100.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_flipped_coordinates",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_limit_output_size() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_limit_output_size",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_single_box() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32)
+        scores = np.array([[[0.9]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_single_box",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_identical_boxes() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.0, 1.0, 1.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array(
+            [[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]
+        ).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_identical_boxes",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_center_point_box_format() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+            center_point_box=1,
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.5, 0.5, 1.0, 1.0],
+                    [0.5, 0.6, 1.0, 1.0],
+                    [0.5, 0.4, 1.0, 1.0],
+                    [0.5, 10.5, 1.0, 1.0],
+                    [0.5, 10.6, 1.0, 1.0],
+                    [0.5, 100.5, 1.0, 1.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
+        max_output_boxes_per_class = np.array([3]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_center_point_box_format",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_two_classes() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ]
+            ]
+        ).astype(np.float32)
+        scores = np.array(
+            [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
+        ).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array(
+            [[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]
+        ).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_two_classes",
+        )
+
+    @staticmethod
+    def export_nonmaxsuppression_two_batches() -> None:
+        node = onnx.helper.make_node(
+            "NonMaxSuppression",
+            inputs=[
+                "boxes",
+                "scores",
+                "max_output_boxes_per_class",
+                "iou_threshold",
+                "score_threshold",
+            ],
+            outputs=["selected_indices"],
+        )
+        boxes = np.array(
+            [
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ],
+                [
+                    [0.0, 0.0, 1.0, 1.0],
+                    [0.0, 0.1, 1.0, 1.1],
+                    [0.0, -0.1, 1.0, 0.9],
+                    [0.0, 10.0, 1.0, 11.0],
+                    [0.0, 10.1, 1.0, 11.1],
+                    [0.0, 100.0, 1.0, 101.0],
+                ],
+            ]
+        ).astype(np.float32)
+        scores = np.array(
+            [[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
+        ).astype(np.float32)
+        max_output_boxes_per_class = np.array([2]).astype(np.int64)
+        iou_threshold = np.array([0.5]).astype(np.float32)
+        score_threshold = np.array([0.0]).astype(np.float32)
+        selected_indices = np.array(
+            [[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]
+        ).astype(np.int64)
+
+        expect(
+            node,
+            inputs=[
+                boxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+            ],
+            outputs=[selected_indices],
+            name="test_nonmaxsuppression_two_batches",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonzero.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonzero.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca2bf184d4f6920c545d1113182e18e12381fd97
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/nonzero.py
@@ -0,0 +1,26 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class NonZero(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "NonZero",
+            inputs=["condition"],
+            outputs=["result"],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        result = np.array(
+            np.nonzero(condition), dtype=np.int64
+        )  # expected output [[0, 1, 1], [0, 0, 1]]
+        expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/not.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/not.py
new file mode 100644
index 0000000000000000000000000000000000000000..3843a7b9ab30c55f9425b8cdbe762b26f06c2151
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/not.py
@@ -0,0 +1,32 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Not(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Not",
+            inputs=["x"],
+            outputs=["not"],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d")
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d")
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/onehot.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/onehot.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff549b2ea32984602b9e8d16d356d1f8f1a5c7f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/onehot.py
@@ -0,0 +1,126 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def one_hot(indices, depth, axis=-1, dtype=np.float32):
+    """Compute one hot from indices at a specific axis"""
+    values = np.asarray(indices)
+    rank = len(values.shape)
+    depth_range = np.arange(depth)
+    if axis < 0:
+        axis += rank + 1
+    ls = values.shape[0:axis]
+    rs = values.shape[axis:rank]
+    targets = np.reshape(
+        depth_range, (1,) * len(ls) + depth_range.shape + (1,) * len(rs)
+    )
+    values = np.reshape(np.mod(values, depth), (*ls, 1, *rs))
+    return np.asarray(targets == values, dtype=dtype)
+
+
+class OneHot(Base):
+    @staticmethod
+    def export_without_axis() -> None:
+        on_value = 5
+        off_value = 2
+        output_type = np.int32
+        node = onnx.helper.make_node(
+            "OneHot", inputs=["indices", "depth", "values"], outputs=["y"]
+        )
+        indices = np.array([0, 7, 8], dtype=np.int64)
+        depth = np.float32(12)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(
+            node,
+            inputs=[indices, depth, values],
+            outputs=[y],
+            name="test_onehot_without_axis",
+        )
+
+    @staticmethod
+    def export_with_axis() -> None:
+        axisValue = 1
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            "OneHot",
+            inputs=["indices", "depth", "values"],
+            outputs=["y"],
+            axis=axisValue,
+        )
+        indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(
+            node,
+            inputs=[indices, depth, values],
+            outputs=[y],
+            name="test_onehot_with_axis",
+        )
+
+    @staticmethod
+    def export_with_negative_indices() -> None:
+        axisValue = 1
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            "OneHot",
+            inputs=["indices", "depth", "values"],
+            outputs=["y"],
+            axis=axisValue,
+        )
+        indices = np.array([0, -7, -8], dtype=np.int64)
+
+        # print(y)
+        # [[3. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
+        #  [1. 1. 1. 3. 1. 1. 1. 1. 1. 1.]
+        #  [1. 1. 3. 1. 1. 1. 1. 1. 1. 1.]]
+
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(
+            node,
+            inputs=[indices, depth, values],
+            outputs=[y],
+            name="test_onehot_negative_indices",
+        )
+
+    @staticmethod
+    def export_with_negative_axis() -> None:
+        axisValue = -2
+        on_value = 3
+        off_value = 1
+        output_type = np.float32
+        node = onnx.helper.make_node(
+            "OneHot",
+            inputs=["indices", "depth", "values"],
+            outputs=["y"],
+            axis=axisValue,
+        )
+        indices = np.array([[1, 9], [2, 4]], dtype=np.float32)
+        depth = np.float32(10)
+        values = np.array([off_value, on_value], dtype=output_type)
+        y = one_hot(indices, depth, axis=axisValue, dtype=output_type)
+        y = y * (on_value - off_value) + off_value
+        expect(
+            node,
+            inputs=[indices, depth, values],
+            outputs=[y],
+            name="test_onehot_with_negative_axis",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalgetelement.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalgetelement.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d7568db9aa181fc267e3312677739f407594cdc
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalgetelement.py
@@ -0,0 +1,80 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def optional_get_element_reference_implementation(optional: Any | None) -> Any:
+    assert optional is not None
+    return optional
+
+
+class OptionalHasElement(Base):
+    @staticmethod
+    def export_get_element_tensor() -> None:
+        optional = np.array([1, 2, 3, 4]).astype(np.float32)
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(
+            elem_type=onnx.TensorProto.FLOAT,
+            shape=[
+                4,
+            ],
+        )
+        optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+        node = onnx.helper.make_node(
+            "OptionalGetElement", inputs=["optional_input"], outputs=["output"]
+        )
+        output = optional_get_element_reference_implementation(optional)
+        expect(
+            node,
+            inputs=[optional],
+            outputs=[output],
+            input_type_protos=[optional_type_proto],
+            name="test_optional_get_element_optional_tensor",
+        )
+        expect(
+            node,
+            inputs=[optional],
+            outputs=[output],
+            input_type_protos=[tensor_type_proto],
+            name="test_optional_get_element_tensor",
+        )
+
+    @staticmethod
+    def export_get_element_sequence() -> None:
+        optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(
+            elem_type=onnx.TensorProto.INT32,
+            shape=[
+                4,
+            ],
+        )
+        seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
+        optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
+
+        node = onnx.helper.make_node(
+            "OptionalGetElement", inputs=["optional_input"], outputs=["output"]
+        )
+        output = optional_get_element_reference_implementation(optional)
+        expect(
+            node,
+            inputs=[optional],
+            outputs=[output],
+            input_type_protos=[optional_type_proto],
+            name="test_optional_get_element_optional_sequence",
+        )
+        expect(
+            node,
+            inputs=[optional],
+            outputs=[output],
+            input_type_protos=[seq_type_proto],
+            name="test_optional_get_element_sequence",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalhaselement.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalhaselement.py
new file mode 100644
index 0000000000000000000000000000000000000000..e24ac7f45a72fd64d9422bf6586399fd41615125
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/optionalhaselement.py
@@ -0,0 +1,94 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def optional_has_element_reference_implementation(
+    optional: np.ndarray | None,
+) -> np.ndarray:
+    if optional is None:
+        return np.array(False)
+    else:
+        return np.array(True)
+
+
+class OptionalHasElement(Base):
+    @staticmethod
+    def export() -> None:
+        optional = np.array([1, 2, 3, 4]).astype(np.float32)
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(
+            elem_type=onnx.TensorProto.FLOAT,
+            shape=[
+                4,
+            ],
+        )
+        optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+        # OptionalHasElement takes a tensor or optional as input
+        for input_type_protos in [tensor_type_proto, optional_type_proto]:
+            node = onnx.helper.make_node(
+                "OptionalHasElement", inputs=["optional_input"], outputs=["output"]
+            )
+            output = optional_has_element_reference_implementation(optional)
+            test_name = "test_optional_has_element_" + (
+                "optional_input"
+                if input_type_protos == optional_type_proto
+                else "tensor_input"
+            )
+            expect(
+                node,
+                inputs=[optional],
+                outputs=[output],
+                input_type_protos=[optional_type_proto],
+                name=test_name,
+            )
+
+    @staticmethod
+    def export_empty() -> None:
+        optional = None
+
+        tensor_type_proto = onnx.helper.make_tensor_type_proto(
+            elem_type=onnx.TensorProto.INT32, shape=[]
+        )
+        optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
+
+        # OptionalHasElement takes a tensor or optional as input
+        for input_type_proto in [tensor_type_proto, optional_type_proto]:
+            input_name_options = {
+                "empty": "optional_input",
+                "empty_no_input_name": "",
+                "empty_no_input": None,
+            }
+            for test_name_surfix, input_name in input_name_options.items():
+                if input_type_proto == tensor_type_proto and input_name:
+                    # the input tensor cannot be empty if input name is provided.
+                    continue
+                node = onnx.helper.make_node(
+                    "OptionalHasElement",
+                    inputs=[] if input_name is None else [input_name],
+                    outputs=["output"],
+                )
+                output = optional_has_element_reference_implementation(optional)
+                test_name = (
+                    "test_optional_has_element_"
+                    + test_name_surfix
+                    + (
+                        "_optional_input"
+                        if input_type_proto == optional_type_proto
+                        else "_tensor_input"
+                    )
+                )
+                expect(
+                    node,
+                    inputs=[optional] if input_name else [],
+                    outputs=[output],
+                    input_type_protos=[input_type_proto] if input_name else [],
+                    name=test_name,
+                )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/or.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/or.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0a86088d0f8be43bf09581ead4e25b76cc454ce
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/or.py
@@ -0,0 +1,76 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Or(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Or",
+            inputs=["x", "y"],
+            outputs=["or"],
+        )
+
+        # 2d
+        x = (np.random.randn(3, 4) > 0).astype(bool)
+        y = (np.random.randn(3, 4) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or2d")
+
+        # 3d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or3d")
+
+        # 4d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or4d")
+
+    @staticmethod
+    def export_or_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Or",
+            inputs=["x", "y"],
+            outputs=["or"],
+        )
+
+        # 3d vs 1d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v1d")
+
+        # 3d vs 2d
+        x = (np.random.randn(3, 4, 5) > 0).astype(bool)
+        y = (np.random.randn(4, 5) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast3v2d")
+
+        # 4d vs 2d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v2d")
+
+        # 4d vs 3d
+        x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
+        y = (np.random.randn(4, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v3d")
+
+        # 4d vs 4d
+        x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
+        y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
+        z = np.logical_or(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_or_bcast4v4d")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pad.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pad.py
new file mode 100644
index 0000000000000000000000000000000000000000..23e996f73c48942d3e3ba7c07df7458c91758599
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pad.py
@@ -0,0 +1,132 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def pad_impl(data, raw_pads, mode, constant_values=0.0, axes=None):
+    input_rank = data.ndim
+    if axes is None:
+        axes = list(range(input_rank))
+    else:
+        axes = [axis if axis >= 0 else axis + input_rank for axis in axes]
+    num_axes = len(axes)
+
+    if num_axes * 2 != raw_pads.size:
+        raise ValueError("The number of elements in raw_pads should be 2 * num_axes")
+
+    pad_width = []
+    for _ in range(input_rank):
+        pad_width += [[0, 0]]  # init to zero
+
+    # re-order to np.pad accepted order ((x1_begin, x1_end), (x2_begin, x2_end), ...)
+    for i in range(num_axes):
+        axis = axes[i]
+        if axis < 0:
+            axis = input_rank + axis
+        pad_width[axis] = [raw_pads[i], raw_pads[i + num_axes]]
+
+    if mode == "constant":
+        y = np.pad(
+            data,
+            pad_width=pad_width,
+            mode=mode,
+            constant_values=constant_values,
+        )
+        return y
+
+    y = np.pad(
+        data,
+        pad_width=pad_width,
+        mode=mode,
+    )
+
+    return y
+
+
+class Pad(Base):
+    @staticmethod
+    def export_constant_pad() -> None:
+        node = onnx.helper.make_node(
+            "Pad", inputs=["x", "pads", "value"], outputs=["y"], mode="constant"
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        pads = np.array([0, 0, 1, 3, 0, 0, 2, 4]).astype(
+            np.int64
+        )  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+        value = np.float32(1.2)
+        y = pad_impl(x, pads, "constant", 1.2)
+
+        expect(node, inputs=[x, pads, value], outputs=[y], name="test_constant_pad")
+
+    @staticmethod
+    def export_reflection_edge_and_wrap_pad() -> None:
+        for mode in ("edge", "reflect", "wrap"):
+            node = onnx.helper.make_node(
+                "Pad", inputs=["x", "pads"], outputs=["y"], mode=mode
+            )
+            x = np.random.randn(1, 3, 4, 5).astype(np.int32)
+            pads = np.array([0, 0, 1, 1, 0, 0, 1, 1]).astype(
+                np.int64
+            )  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+            y = pad_impl(x, pads, mode)
+
+            expect(node, inputs=[x, pads], outputs=[y], name=f"test_{mode}_pad")
+
+    @staticmethod
+    def export_constant_pad_axes() -> None:
+        node = onnx.helper.make_node(
+            "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant"
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        pads = np.array([0, 3, 0, 4]).astype(
+            np.int64
+        )  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+        value = np.float32(1.2)
+        axes = np.array([1, 3], dtype=np.int64)
+        y = pad_impl(
+            x,
+            pads,
+            "constant",
+            1.2,
+            [1, 3],
+        )
+
+        expect(
+            node,
+            inputs=[x, pads, value, axes],
+            outputs=[y],
+            name="test_constant_pad_axes",
+        )
+
+    @staticmethod
+    def export_constant_pad_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            "Pad", inputs=["x", "pads", "value", "axes"], outputs=["y"], mode="constant"
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        pads = np.array([0, 3, 0, 4]).astype(
+            np.int64
+        )  # pad order [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
+        value = np.float32(1.2)
+        axes = np.array([-3, -1], dtype=np.int64)
+        y = pad_impl(
+            x,
+            pads,
+            "constant",
+            1.2,
+            [-3, -1],
+        )
+
+        expect(
+            node,
+            inputs=[x, pads, value, axes],
+            outputs=[y],
+            name="test_constant_pad_negative_axes",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pow.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pow.py
new file mode 100644
index 0000000000000000000000000000000000000000..29219c86a967a5a9c00d9638622990458640b1b7
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/pow.py
@@ -0,0 +1,107 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def pow(x, y):  # type: ignore  # noqa: A001
+    z = np.power(x, y).astype(x.dtype)
+    return z
+
+
+class Pow(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Pow",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_example")
+
+        x = np.arange(60).reshape(3, 4, 5).astype(np.float32)
+        y = np.random.randn(3, 4, 5).astype(np.float32)
+        z = pow(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow")
+
+    @staticmethod
+    def export_pow_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "Pow",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array(2).astype(np.float32)
+        z = pow(x, y)  # expected output [1., 4., 9.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_scalar")
+
+        node = onnx.helper.make_node(
+            "Pow",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+        x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
+        y = np.array([1, 2, 3]).astype(np.float32)
+        # expected output [[1, 4, 27], [4, 25, 216]]
+        z = pow(x, y)
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_bcast_array")
+
+    @staticmethod
+    def export_types() -> None:
+        node = onnx.helper.make_node(
+            "Pow",
+            inputs=["x", "y"],
+            outputs=["z"],
+        )
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.int64)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int64")
+
+        x = np.array([1, 2, 3]).astype(np.int64)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_float32")
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.int32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_int32")
+
+        x = np.array([1, 2, 3]).astype(np.int32)
+        y = np.array([4, 5, 6]).astype(np.float32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_float32")
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.uint64)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint64")
+
+        x = np.array([1, 2, 3]).astype(np.float32)
+        y = np.array([4, 5, 6]).astype(np.uint32)
+        z = pow(x, y)  # expected output [1., 32., 729.]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_float32_uint32")
+
+        x = np.array([1, 2, 3]).astype(np.int64)
+        y = np.array([4, 5, 6]).astype(np.int64)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int64_int64")
+
+        x = np.array([1, 2, 3]).astype(np.int32)
+        y = np.array([4, 5, 6]).astype(np.int32)
+        z = pow(x, y)  # expected output [1, 32, 729]
+        expect(node, inputs=[x, y], outputs=[z], name="test_pow_types_int32_int32")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/prelu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/prelu.py
new file mode 100644
index 0000000000000000000000000000000000000000..069d49550754736e62032fb60647a9221b621588
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/prelu.py
@@ -0,0 +1,40 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class PRelu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "PRelu",
+            inputs=["x", "slope"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        slope = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+        expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example")
+
+    @staticmethod
+    def export_prelu_broadcast() -> None:
+        node = onnx.helper.make_node(
+            "PRelu",
+            inputs=["x", "slope"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        slope = np.random.randn(5).astype(np.float32)
+        y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+
+        expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearconv.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearconv.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff547803d1beee43113e09911181a7af195e9515
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearconv.py
@@ -0,0 +1,82 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class QLinearConv(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "QLinearConv",
+            inputs=[
+                "x",
+                "x_scale",
+                "x_zero_point",
+                "w",
+                "w_scale",
+                "w_zero_point",
+                "y_scale",
+                "y_zero_point",
+            ],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                [255, 174, 162, 25, 203, 168, 58],
+                [15, 59, 237, 95, 129, 0, 64],
+                [56, 242, 153, 221, 168, 12, 166],
+                [232, 178, 186, 195, 237, 162, 237],
+                [188, 39, 124, 77, 80, 102, 43],
+                [127, 230, 21, 83, 41, 40, 134],
+                [255, 154, 92, 141, 42, 148, 247],
+            ],
+            dtype=np.uint8,
+        ).reshape((1, 1, 7, 7))
+
+        x_scale = np.float32(0.00369204697)
+        x_zero_point = np.uint8(132)
+
+        w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+        w_scale = np.array([0.00172794575], dtype=np.float32)
+        w_zero_point = np.array([255], dtype=np.uint8)
+
+        y_scale = np.float32(0.00162681262)
+        y_zero_point = np.uint8(123)
+
+        output = np.array(
+            [
+                [0, 81, 93, 230, 52, 87, 197],
+                [240, 196, 18, 160, 126, 255, 191],
+                [199, 13, 102, 34, 87, 243, 89],
+                [23, 77, 69, 60, 18, 93, 18],
+                [67, 216, 131, 178, 175, 153, 212],
+                [128, 25, 234, 172, 214, 215, 121],
+                [0, 101, 163, 114, 213, 107, 8],
+            ],
+            dtype=np.uint8,
+        ).reshape((1, 1, 7, 7))
+
+        expect(
+            node,
+            inputs=[
+                x,
+                x_scale,
+                x_zero_point,
+                w,
+                w_scale,
+                w_zero_point,
+                y_scale,
+                y_zero_point,
+            ],
+            outputs=[output],
+            name="test_qlinearconv",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearmatmul.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearmatmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..efd5fd68ed348248d357bdeef538a6fee5c59dba
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/qlinearmatmul.py
@@ -0,0 +1,157 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class QLinearMatMul(Base):
+    @staticmethod
+    def export_int() -> None:
+        for quant_type_name in ["uint8", "int8"]:
+            quant_type = getattr(np, quant_type_name)
+            for dtype_name in ["float32", "float16"]:
+                dtype = getattr(np, dtype_name)
+                node = onnx.helper.make_node(
+                    "QLinearMatMul",
+                    inputs=[
+                        "a",
+                        "a_scale",
+                        "a_zero_point",
+                        "b",
+                        "b_scale",
+                        "b_zero_point",
+                        "y_scale",
+                        "y_zero_point",
+                    ],
+                    outputs=["y"],
+                )
+
+                # 2D
+                a = np.array([[208, 236, 0, 238], [3, 214, 255, 29]])
+                if quant_type == np.int8:
+                    a -= 127
+                a = a.astype(quant_type)
+
+                a_scale = np.array([0.0066], dtype=dtype)
+                a_zero_point = np.array(
+                    [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type
+                )
+
+                b = np.array(
+                    [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]]
+                )
+                if quant_type == np.int8:
+                    b -= 127
+                b = b.astype(quant_type)
+
+                b_scale = np.array([0.00705], dtype=dtype)
+                b_zero_point = np.array(
+                    [114 - 127] if quant_type == np.int8 else [114], dtype=quant_type
+                )
+
+                y_scale = np.array([0.0107], dtype=dtype)
+                y_zero_point = np.array(
+                    [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type
+                )
+
+                if quant_type == np.int8:
+                    output = np.array([[41, -12, -9], [1, -75, 20]])
+                else:
+                    output = np.array([[168, 115, 255], [1, 66, 151]])
+                output = output.astype(quant_type)
+
+                expect(
+                    node,
+                    inputs=[
+                        a,
+                        a_scale,
+                        a_zero_point,
+                        b,
+                        b_scale,
+                        b_zero_point,
+                        y_scale,
+                        y_zero_point,
+                    ],
+                    outputs=[output],
+                    name=f"test_qlinearmatmul_2D_{quant_type_name}_{dtype_name}",
+                )
+
+                # 3D
+                a = np.array(
+                    [
+                        [[208, 236, 0, 238], [3, 214, 255, 29]],
+                        [[208, 236, 0, 238], [3, 214, 255, 29]],
+                    ],
+                )
+                if quant_type == np.int8:
+                    a -= 127
+                a = a.astype(quant_type)
+
+                a_scale = np.array([0.0066], dtype=dtype)
+                a_zero_point = np.array(
+                    [113 - 127] if quant_type == np.int8 else [113], dtype=quant_type
+                )
+
+                b = np.array(
+                    [
+                        [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
+                        [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
+                    ],
+                )
+                if quant_type == np.int8:
+                    b -= 127
+                b = b.astype(quant_type)
+
+                b_scale = np.array([0.00705], dtype=dtype)
+                b_zero_point = np.array([114], dtype=quant_type)
+
+                y_scale = np.array([0.0107], dtype=dtype)
+                y_zero_point = np.array(
+                    [118 - 127] if quant_type == np.int8 else [118], dtype=quant_type
+                )
+
+                if quant_type == np.int8:
+                    if dtype == np.float32:
+                        output = np.array(
+                            [
+                                [[-86, 117, 120], [115, 39, -121]],
+                                [[-86, 117, 120], [115, 39, -121]],
+                            ]
+                        )
+                    else:
+                        output = np.array(
+                            [
+                                [[-86, 116, 119], [115, 39, -121]],
+                                [[-86, 116, 119], [115, 39, -121]],
+                            ]
+                        )
+                else:
+                    output = np.array(
+                        [
+                            [[168, 115, 255], [1, 66, 151]],
+                            [[168, 115, 255], [1, 66, 151]],
+                        ]
+                    )
+                output = output.astype(quant_type)
+
+                expect(
+                    node,
+                    inputs=[
+                        a,
+                        a_scale,
+                        a_zero_point,
+                        b,
+                        b_scale,
+                        b_zero_point,
+                        y_scale,
+                        y_zero_point,
+                    ],
+                    outputs=[output],
+                    name=f"test_qlinearmatmul_3D_{quant_type_name}_{dtype_name}",
+                )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/quantizelinear.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/quantizelinear.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb9f286c51651b21760dc4203c4ba10320bb5467
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/quantizelinear.py
@@ -0,0 +1,438 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx import TensorProto
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.helper import make_tensor
+
+
+class QuantizeLinear(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+        y_scale = np.float32(2)
+        y_zero_point = np.uint8(128)
+        y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear",
+        )
+
+    @staticmethod
+    def export_axis() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                [
+                    [[-162, 10], [-100, 232], [-20, -50]],
+                    [[-76, 0], [0, 252], [32, -44]],
+                    [[245, -485], [-960, -270], [-375, -470]],
+                ],
+            ],
+            dtype=np.float32,
+        )
+        y_scale = np.array([2, 4, 5], dtype=np.float32)
+        y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
+        y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(
+            np.uint8
+        )
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_axis",
+        )
+
+    @staticmethod
+    def export_e4m3fn() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
+        y_scale = np.float32(2)
+        y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E4M3FN, [1], [0])
+        y = make_tensor("y", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, 96])
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_e4m3fn",
+        )
+
+    @staticmethod
+    def export_e5m2() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array([0.0, 1.0, 2.0, 100000.0, 200.0]).astype(np.float32)
+        y_scale = np.float32(2)
+        y_zero_point = make_tensor("y_zero_point", TensorProto.FLOAT8E5M2, [1], [0.0])
+        y = make_tensor("y", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, 96])
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_e5m2",
+        )
+
+    @staticmethod
+    def export_uint16() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                0.0,
+                -128.0,
+                3.0,
+                -3.0,
+                2.9,
+                -2.9,
+                3.1,
+                -3.1,
+                65536.0,
+                -65534.0,
+                70000.0,
+                -70000.0,
+            ]
+        ).astype(np.float32)
+        y_scale = np.float32(2.0)
+        y_zero_point = np.uint16(32767)
+        y = np.array(
+            [
+                32767,
+                32703,
+                32769,
+                32765,
+                32768,
+                32766,
+                32769,
+                32765,
+                65535,
+                0,
+                65535,
+                0,
+            ]
+        ).astype(np.uint16)
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_uint16",
+        )
+
+    @staticmethod
+    def export_int16() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                0.0,
+                -514.0,
+                3.0,
+                -3.0,
+                2.9,
+                -2.9,
+                3.1,
+                -3.1,
+                65022.0,
+                -66046.0,
+                65023.0,
+                -66047.0,
+                65024.0,
+                -66048.0,
+                70000.0,
+                -70000.0,
+            ]
+        ).astype(np.float32)
+        y_scale = np.float32(2.0)
+        y_zero_point = np.int16(256)
+        y = np.array(
+            [
+                256,
+                -1,
+                258,
+                254,
+                257,
+                255,
+                258,
+                254,
+                32767,
+                -32767,
+                32767,
+                -32768,
+                32767,
+                -32768,
+                32767,
+                -32768,
+            ]
+        ).astype(np.int16)
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_int16",
+        )
+
+    @staticmethod
+    def export_uint4() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+            axis=0,
+        )
+
+        x = np.array(
+            [
+                [0.0, 2.5, 4.8, 8.6],
+                [-30, -20, 6, 9],
+                [12, 15, 16, 40],
+            ]
+        ).astype(np.float32)
+
+        y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
+        y_zero_point = make_tensor(
+            "y_zero_point", TensorProto.UINT4, y_scale.shape, np.ones_like(y_scale)
+        )
+        y = make_tensor(
+            "y", TensorProto.UINT4, x.shape, [1, 2, 3, 5, 0, 0, 3, 4, 4, 5, 5, 11]
+        )
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_uint4",
+        )
+
+    @staticmethod
+    def export_int4() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+            axis=0,
+        )
+
+        x = np.array(
+            [
+                [0.0, 2.5, 4.8, 8.6],
+                [-30, -20, 6, 9],
+                [12, 15, 16, 40],
+            ]
+        ).astype(np.float32)
+
+        y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
+        y_zero_point = make_tensor(
+            "y_zero_point", TensorProto.INT4, y_scale.shape, np.ones_like(y_scale)
+        )
+        y = make_tensor(
+            "y", TensorProto.INT4, x.shape, [1, 2, 3, 5, -8, -6, 3, 4, 4, 5, 5, 7]
+        )
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_int4",
+        )
+
+    @staticmethod
+    def export_float4e2m1() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+            axis=0,
+        )
+
+        x = np.array(
+            [
+                [0.0, 2.5, 4.8, 8.6],
+                [-30, -20, 6, 9],
+                [-0.0, -2.5, -4.8, -8.6],
+            ]
+        ).astype(np.float32)
+
+        y_scale = np.asarray([2.0, 3.0, 4.0], dtype=np.float32)
+        y_zero_point = make_tensor(
+            "y_zero_point",
+            TensorProto.FLOAT4E2M1,
+            y_scale.shape,
+            np.zeros_like(y_scale),
+        )
+        y = make_tensor(
+            "y",
+            TensorProto.FLOAT4E2M1,
+            x.shape,
+            [0, 1, 2, 4, -6, -6, 2, 3, 0, -0.5, -1, -2],
+        )
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_float4e2m1",
+        )
+
+    @staticmethod
+    def export_blocked_asymmetric() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale", "y_zero_point"],
+            outputs=["y"],
+            axis=1,
+            block_size=2,
+        )
+
+        x = np.array(
+            [
+                [6.0, 12.0, 50.0, 5.0],
+                [1.0, 8.0, 4.0, 5.0],
+                [0.0, 20.0, 10.0, 4.0],
+            ],
+            dtype=np.float32,
+        )
+        y_scale = np.array(
+            [
+                [1.5, 2.5],
+                [3.0, 4.9],
+                [5.1, 6.9],
+            ],
+            dtype=np.float32,
+        )
+        y_zero_point = np.array(
+            [
+                [0, 1],
+                [1, 0],
+                [2, 3],
+            ],
+            dtype=np.uint8,
+        )
+        # x.shape = (3, 4)
+        # y_scale.shape = (3, 2)
+        assert y_scale.shape == y_zero_point.shape
+        block_axis = 1
+        # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2)
+        assert all(
+            x.shape[i] == y_scale.shape[i]
+            for i in range(len(x.shape))
+            if i != block_axis
+        )
+        assert x.shape[block_axis] % y_scale.shape[block_axis] == 0
+        repeats = x.shape[block_axis] // y_scale.shape[block_axis]
+
+        # Create element-wise scale and zero point
+        y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis)
+        y_zero_point_elementwise = np.repeat(
+            y_zero_point, repeats=repeats, axis=block_axis
+        )
+
+        y = np.rint(x / y_scale_elementwise + y_zero_point_elementwise).astype(np.uint8)
+
+        expect(
+            node,
+            inputs=[x, y_scale, y_zero_point],
+            outputs=[y],
+            name="test_quantizelinear_blocked_asymmetric",
+        )
+
+    @staticmethod
+    def export_blocked_symmetric() -> None:
+        node = onnx.helper.make_node(
+            "QuantizeLinear",
+            inputs=["x", "y_scale"],
+            outputs=["y"],
+            axis=1,
+            block_size=2,
+            output_dtype=TensorProto.INT16,
+        )
+
+        x = np.array(
+            [
+                [6.0, -8, -10, 5.0],
+                [1.0, 8.0, 4.0, 5.0],
+                [0.0, 20.0, 10.0, 4.0],
+            ],
+            dtype=np.float32,
+        )
+
+        y_scale = np.array(
+            [
+                [1.5, 2.5],
+                [3.0, 4.9],
+                [5.1, 6.9],
+            ],
+            dtype=np.float32,
+        )
+
+        # x.shape = (3, 4)
+        # y_scale.shape = (3, 2)
+
+        block_axis = 1
+        # The block shape is [x.shape[i] // y_scale.shape[i] for i in range(len(x.shape))] = (1, 2)
+        assert all(
+            x.shape[i] == y_scale.shape[i]
+            for i in range(len(x.shape))
+            if i != block_axis
+        )
+        assert x.shape[block_axis] % y_scale.shape[block_axis] == 0
+        repeats = x.shape[block_axis] // y_scale.shape[block_axis]
+
+        # Create element-wise scale and zero point
+        y_scale_elementwise = np.repeat(y_scale, repeats=repeats, axis=block_axis)
+
+        y_val = np.clip(
+            np.rint(x / y_scale_elementwise), a_min=-32768, a_max=32767
+        ).astype(np.int16)
+        y = make_tensor(
+            "y",
+            TensorProto.INT16,
+            x.shape,
+            y_val,
+        )
+        expect(
+            node,
+            inputs=[x, y_scale],
+            outputs=[y],
+            name="test_quantizelinear_blocked_symmetric",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rangeop.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rangeop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2c0b8fc29b91235d67f44708617927db26e1189
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rangeop.py
@@ -0,0 +1,56 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Range(Base):
+    @staticmethod
+    def export_range_float_type_positive_delta() -> None:
+        node = onnx.helper.make_node(
+            "Range",
+            inputs=["start", "limit", "delta"],
+            outputs=["output"],
+        )
+
+        start = np.float32(1)
+        limit = np.float32(5)
+        delta = np.float32(2)
+
+        output = np.arange(
+            start, limit, delta, dtype=np.float32
+        )  # expected output [1.0, 3.0]
+        expect(
+            node,
+            inputs=[start, limit, delta],
+            outputs=[output],
+            name="test_range_float_type_positive_delta",
+        )
+
+    @staticmethod
+    def export_range_int32_type_negative_delta() -> None:
+        node = onnx.helper.make_node(
+            "Range",
+            inputs=["start", "limit", "delta"],
+            outputs=["output"],
+        )
+
+        start = np.int32(10)
+        limit = np.int32(6)
+        delta = np.int32(-3)
+
+        output = np.arange(
+            start, limit, delta, dtype=np.int32
+        )  # expected output [10, 7]
+        expect(
+            node,
+            inputs=[start, limit, delta],
+            outputs=[output],
+            name="test_range_int32_type_negative_delta",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reciprocal.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reciprocal.py
new file mode 100644
index 0000000000000000000000000000000000000000..3322e070fe528e26a02c0ddf0c64131fadbbdb3a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reciprocal.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Reciprocal(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Reciprocal",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-4, 2]).astype(np.float32)
+        y = np.reciprocal(x)  # expected output [-0.25, 0.5],
+        expect(node, inputs=[x], outputs=[y], name="test_reciprocal_example")
+
+        x = np.random.rand(3, 4, 5).astype(np.float32) + 0.5
+        y = np.reciprocal(x)
+        expect(node, inputs=[x], outputs=[y], name="test_reciprocal")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum.py
new file mode 100644
index 0000000000000000000000000000000000000000..aee869f213c6ebe8ff4dfe0188f999cec954f681
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum.py
@@ -0,0 +1,104 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ReduceLogSum(Base):
+    @staticmethod
+    def export_nokeepdims() -> None:
+        shape = [3, 4, 5]
+        axes = np.array([2, 1], dtype=np.int64)
+
+        node = onnx.helper.make_node(
+            "ReduceLogSum",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=0,
+        )
+        data = np.random.ranf(shape).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_desc_axes",
+        )
+
+        axes = np.array([0, 1], dtype=np.int64)
+        node = onnx.helper.make_node(
+            "ReduceLogSum",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=0,
+        )
+        data = np.random.ranf(shape).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_asc_axes",
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        node = onnx.helper.make_node(
+            "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, keepdims=True))
+        axes = np.array([], dtype=np.int64)
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_default",
+        )
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        axes = np.array([-2], dtype=np.int64)
+        node = onnx.helper.make_node(
+            "ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
+        )
+        data = np.random.ranf([3, 4, 5]).astype(np.float32)
+        reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True))
+        # print(reduced)
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_negative_axes",
+        )
+
+    @staticmethod
+    def export_empty_set() -> None:
+        shape = [2, 0, 4]
+        keepdims = 1
+        reduced_shape = [2, 1, 4]
+
+        node = onnx.helper.make_node(
+            "ReduceLogSum",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array([], dtype=np.float32).reshape(shape)
+        axes = np.array([1], dtype=np.int64)
+        zero = np.array(np.zeros(reduced_shape, dtype=np.float32))
+        reduced = np.log(zero)  # -inf
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_empty_set",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum_exp.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum_exp.py
new file mode 100644
index 0000000000000000000000000000000000000000..50b7dbb7080723ab38bdfb0f6c24f1edec8d1591
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reduce_log_sum_exp.py
@@ -0,0 +1,193 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ReduceLogSumExp(Base):
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([1], dtype=np.int64)
+        keepdims = 0
+        node = onnx.helper.make_node(
+            "ReduceLogSumExp",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
+        )
+        reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        # [[20., 2.31326175]
+        # [40.00004578, 2.31326175]
+        # [60.00671387, 2.31326175]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_do_not_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_do_not_keepdims_random",
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([1], dtype=np.int64)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ReduceLogSumExp",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
+        )
+        reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[20., 2.31326175]]
+        # [[40.00004578, 2.31326175]]
+        # [[60.00671387, 2.31326175]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_keepdims_random",
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceLogSumExp",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
+        )
+        reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[60.00671387]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_default_axes_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_default_axes_keepdims_random",
+        )
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([-2], dtype=np.int64)
+        keepdims = 1
+        node = onnx.helper.make_node(
+            "ReduceLogSumExp",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
+        )
+        reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[20., 2.31326175]]
+        # [[40.00004578, 2.31326175]]
+        # [[60.00671387, 2.31326175]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_negative_axes_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.double)
+        reduced = np.log(
+            np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1)
+        )
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_negative_axes_keepdims_random",
+        )
+
+    @staticmethod
+    def export_empty_set() -> None:
+        shape = [2, 0, 4]
+        keepdims = 1
+        reduced_shape = [2, 1, 4]
+
+        node = onnx.helper.make_node(
+            "ReduceLogSumExp",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array([], dtype=np.float32).reshape(shape)
+        axes = np.array([1], dtype=np.int64)
+        zero = np.array(np.zeros(reduced_shape, dtype=np.float32))
+        reduced = np.log(zero)  # -inf
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_log_sum_exp_empty_set",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel1.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel1.py
new file mode 100644
index 0000000000000000000000000000000000000000..51fc52627f2f3371fb7d5857927bc72979628cf6
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel1.py
@@ -0,0 +1,189 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ReduceL1(Base):
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([2], dtype=np.int64)
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            "ReduceL1",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        # [[3., 7.], [11., 15.], [19., 23.]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_do_not_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_do_not_keepdims_random",
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([2], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL1",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_keep_dims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_keep_dims_random",
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
+        # print(reduced)
+        # [[[78.]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_default_axes_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_default_axes_keepdims_random",
+        )
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([-1], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL1",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        # print(reduced)
+        # [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_negative_axes_keep_dims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_negative_axes_keep_dims_random",
+        )
+
+    @staticmethod
+    def export_empty_set() -> None:
+        shape = [2, 0, 4]
+        keepdims = 1
+        reduced_shape = [2, 1, 4]
+
+        node = onnx.helper.make_node(
+            "ReduceL1",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array([], dtype=np.float32).reshape(shape)
+        axes = np.array([1], dtype=np.int64)
+        reduced = np.array(np.zeros(reduced_shape, dtype=np.float32))
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l1_empty_set",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel2.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eaa6359c5e999d3c7184f050dc0dc54ea73f2c2
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reducel2.py
@@ -0,0 +1,207 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ReduceL2(Base):
+    @staticmethod
+    def export_do_not_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([2], dtype=np.int64)
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            "ReduceL2",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+        # print(reduced)
+        # [[2.23606798, 5.],
+        # [7.81024968, 10.63014581],
+        # [13.45362405, 16.2788206]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_do_not_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_do_not_keepdims_random",
+        )
+
+    @staticmethod
+    def export_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([2], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL2",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+        # print(reduced)
+        # [[[2.23606798], [5.]]
+        # [[7.81024968], [10.63014581]]
+        # [[13.45362405], [16.2788206 ]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_keep_dims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_keep_dims_random",
+        )
+
+    @staticmethod
+    def export_default_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[25.49509757]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_default_axes_keepdims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_default_axes_keepdims_random",
+        )
+
+    @staticmethod
+    def export_negative_axes_keepdims() -> None:
+        shape = [3, 2, 2]
+        axes = np.array([-1], dtype=np.int64)
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            "ReduceL2",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        # print(data)
+        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+        # print(reduced)
+        # [[[2.23606798], [5.]]
+        # [[7.81024968], [10.63014581]]
+        # [[13.45362405], [16.2788206 ]]]
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_negative_axes_keep_dims_example",
+        )
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(
+            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
+        )
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_negative_axes_keep_dims_random",
+        )
+
+    @staticmethod
+    def export_empty_set() -> None:
+        shape = [2, 0, 4]
+        keepdims = 1
+        reduced_shape = [2, 1, 4]
+
+        node = onnx.helper.make_node(
+            "ReduceL2",
+            inputs=["data", "axes"],
+            outputs=["reduced"],
+            keepdims=keepdims,
+        )
+
+        data = np.array([], dtype=np.float32).reshape(shape)
+        axes = np.array([1], dtype=np.int64)
+        reduced = np.array(np.zeros(reduced_shape, dtype=np.float32))
+
+        expect(
+            node,
+            inputs=[data, axes],
+            outputs=[reduced],
+            name="test_reduce_l2_empty_set",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/regex_full_match.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/regex_full_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..74619205481da741ff840b6fedfc5b063513ec11
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/regex_full_match.py
@@ -0,0 +1,68 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class RegexFullMatch(Base):
+    @staticmethod
+    def export_basic() -> None:
+        node = onnx.helper.make_node(
+            "RegexFullMatch",
+            inputs=["X"],
+            outputs=["Y"],
+            pattern=r"www\.[\w.-]+\.\bcom\b",
+        )
+
+        x = np.array(["www.google.com", "www.facebook.com", "www.bbc.co.uk"]).astype(
+            object
+        )
+        result = np.array([True, True, False])
+        expect(node, inputs=[x], outputs=[result], name="test_regex_full_match_basic")
+
+    @staticmethod
+    def export_match_email_domain() -> None:
+        node = onnx.helper.make_node(
+            "RegexFullMatch",
+            inputs=["X"],
+            outputs=["Y"],
+            pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
+        )
+
+        x = np.array(
+            [
+                ["account@gmail.com", "account@hotmail.com"],
+                ["not email", "account2@yahoo.com"],
+            ]
+        ).astype(object)
+        result = np.array([[True, False], [False, True]])
+        expect(
+            node,
+            inputs=[x],
+            outputs=[result],
+            name="test_regex_full_match_email_domain",
+        )
+
+    @staticmethod
+    def export_match_empty() -> None:
+        node = onnx.helper.make_node(
+            "RegexFullMatch",
+            inputs=["X"],
+            outputs=["Y"],
+            pattern=r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
+        )
+
+        x = np.array([[], []]).astype(object)
+        result = np.array([[], []]).astype(bool)
+        expect(
+            node,
+            inputs=[x],
+            outputs=[result],
+            name="test_regex_full_match_empty",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/relu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e8b1ea7f38eddc02c03634e0b0cddf15fc81e12
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/relu.py
@@ -0,0 +1,24 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Relu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Relu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, 0, np.inf)
+
+        expect(node, inputs=[x], outputs=[y], name="test_relu")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reshape.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eb06d1310ec4af05a9d8faeabd9b5454429c29f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reshape.py
@@ -0,0 +1,83 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def reshape_reference_implementation(
+    data: np.ndarray, shape: np.ndarray, allowzero: int = 0
+) -> np.ndarray:
+    # replace zeros with corresponding dim size
+    # we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set
+    new_shape = np.copy(shape)
+    if allowzero == 0:
+        zeros_index = np.where(shape == 0)
+        new_shape[zeros_index] = np.array(data.shape)[zeros_index]
+    reshaped = np.reshape(data, new_shape)
+    return reshaped
+
+
+class Reshape(Base):
+    @staticmethod
+    def export_reshape() -> None:
+        original_shape = [2, 3, 4]
+        test_cases = {
+            "reordered_all_dims": np.array([4, 2, 3], dtype=np.int64),
+            "reordered_last_dims": np.array([2, 4, 3], dtype=np.int64),
+            "reduced_dims": np.array([2, 12], dtype=np.int64),
+            "extended_dims": np.array([2, 3, 2, 2], dtype=np.int64),
+            "one_dim": np.array([24], dtype=np.int64),
+            "negative_dim": np.array([2, -1, 2], dtype=np.int64),
+            "negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64),
+            "zero_dim": np.array([2, 0, 4, 1], dtype=np.int64),
+            "zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64),
+        }
+        data = np.random.random_sample(original_shape).astype(np.float32)
+
+        for test_name, shape in test_cases.items():
+            node = onnx.helper.make_node(
+                "Reshape",
+                inputs=["data", "shape"],
+                outputs=["reshaped"],
+            )
+
+            reshaped = reshape_reference_implementation(data, shape)
+
+            expect(
+                node,
+                inputs=[data, shape],
+                outputs=[reshaped],
+                name="test_reshape_" + test_name,
+            )
+
+    @staticmethod
+    def export_allowzero() -> None:
+        original_shape = [0, 3, 4]
+        test_cases = {
+            "allowzero_reordered": np.array([3, 4, 0], dtype=np.int64),
+        }
+        data = np.random.random_sample(original_shape).astype(np.float32)
+
+        for test_name, shape in test_cases.items():
+            node = onnx.helper.make_node(
+                "Reshape",
+                inputs=["data", "shape"],
+                outputs=["reshaped"],
+                allowzero=1,  # if allowzero=1, final shape = (3, 4, 0)
+                # if allowzero=0, final shape = (3, 4, 4)
+            )
+
+            reshaped = reshape_reference_implementation(data, shape, allowzero=1)
+
+            expect(
+                node,
+                inputs=[data, shape],
+                outputs=[reshaped],
+                name="test_reshape_" + test_name,
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/resize.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/resize.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb736d71423dc01a3bf65d355e6dd0bff1ef7dcd
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/resize.py
@@ -0,0 +1,1714 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_resize import _cubic_coeffs as cubic_coeffs
+from onnx.reference.ops.op_resize import (
+    _cubic_coeffs_antialias as cubic_coeffs_antialias,
+)
+from onnx.reference.ops.op_resize import _interpolate_nd as interpolate_nd
+from onnx.reference.ops.op_resize import _linear_coeffs as linear_coeffs
+from onnx.reference.ops.op_resize import (
+    _linear_coeffs_antialias as linear_coeffs_antialias,
+)
+from onnx.reference.ops.op_resize import _nearest_coeffs as nearest_coeffs
+
+
+class Resize(Base):
+    @staticmethod
+    def export_resize_upsample_scales_nearest() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+        # [[[[1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 2. 2. 2.]
+        #    [3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_nearest",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_nearest() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[1. 3.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_nearest",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 7, 8], dtype=np.int64)
+
+        # [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_nearest() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 1, 3], dtype=np.int64)
+
+        # [[[[1. 2. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_nearest",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_linear() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[1.   1.25 1.75 2.  ]
+        #    [1.5  1.75 2.25 2.5 ]
+        #    [2.5  2.75 3.25 3.5 ]
+        #    [3.   3.25 3.75 4.  ]]]]
+        output = interpolate_nd(
+            data, lambda x, _: linear_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_linear",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_linear_align_corners() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="align_corners",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[1.         1.33333333 1.66666667 2.        ]
+        #    [1.66666667 2.         2.33333333 2.66666667]
+        #    [2.33333333 2.66666667 3.         3.33333333]
+        #    [3.         3.33333333 3.66666667 4.        ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_linear_align_corners",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_linear() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[2.6666665 4.3333331]]]]
+        output = interpolate_nd(
+            data, lambda x, _: linear_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_linear",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_linear_align_corners() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="align_corners",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[1.       3.142857]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_linear_align_corners",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_cubic() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 0.47265625  0.76953125  1.24609375  1.875       2.28125
+        #      2.91015625  3.38671875  3.68359375]
+        #    [ 1.66015625  1.95703125  2.43359375  3.0625      3.46875
+        #      4.09765625  4.57421875  4.87109375]
+        #    [ 3.56640625  3.86328125  4.33984375  4.96875     5.375
+        #      6.00390625  6.48046875  6.77734375]
+        #    [ 6.08203125  6.37890625  6.85546875  7.484375    7.890625
+        #      8.51953125  8.99609375  9.29296875]
+        #    [ 7.70703125  8.00390625  8.48046875  9.109375    9.515625
+        #     10.14453125 10.62109375 10.91796875]
+        #    [10.22265625 10.51953125 10.99609375 11.625      12.03125
+        #     12.66015625 13.13671875 13.43359375]
+        #    [12.12890625 12.42578125 12.90234375 13.53125    13.9375
+        #     14.56640625 15.04296875 15.33984375]
+        #    [13.31640625 13.61328125 14.08984375 14.71875    15.125
+        #     15.75390625 16.23046875 16.52734375]]]]
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_cubic",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_cubic_align_corners() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            coordinate_transformation_mode="align_corners",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 1.          1.34110787  1.80029155  2.32944606  2.67055394
+        #      3.19970845  3.65889213  4.        ]
+        #    [ 2.36443149  2.70553936  3.16472303  3.69387755  4.03498542
+        #      4.56413994  5.02332362  5.36443149]
+        #    [ 4.20116618  4.54227405  5.00145773  5.53061224  5.87172012
+        #      6.40087464  6.86005831  7.20116618]
+        #    [ 6.31778426  6.65889213  7.1180758   7.64723032  7.98833819
+        #      8.51749271  8.97667638  9.31778426]
+        #    [ 7.68221574  8.02332362  8.48250729  9.01166181  9.35276968
+        #      9.8819242  10.34110787 10.68221574]
+        #    [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
+        #     11.99854227 12.45772595 12.79883382]
+        #    [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
+        #     13.83527697 14.29446064 14.63556851]
+        #    [13.         13.34110787 13.80029155 14.32944606 14.67055394
+        #     15.19970845 15.65889213 16.        ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_cubic_align_corners",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.47119141  2.78125     4.08251953]
+        #    [ 6.71142578  8.02148438  9.32275391]
+        #    [11.91650391 13.2265625  14.52783203]]]]
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_cubic",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic_align_corners() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            coordinate_transformation_mode="align_corners",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.          2.39519159  3.79038317]
+        #    [ 6.58076634  7.97595793  9.37114951]
+        #    [12.16153268 13.55672427 14.95191585]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_cubic_align_corners",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_cubic() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="cubic",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 9, 10], dtype=np.int64)
+
+        # [[[[ 0.45507922  0.64057922  0.97157922  1.42257922  1.90732922
+        #      2.22332922  2.70807922  3.15907922  3.49007922  3.67557922]
+        #    [ 1.39437963  1.57987963  1.91087963  2.36187963  2.84662963
+        #      3.16262963  3.64737963  4.09837963  4.42937963  4.61487963]
+        #    [ 2.95130693  3.13680693  3.46780693  3.91880693  4.40355693
+        #      4.71955693  5.20430693  5.65530693  5.98630693  6.17180693]
+        #    [ 5.20525069  5.39075069  5.72175069  6.17275069  6.65750069
+        #      6.97350069  7.45825069  7.90925069  8.24025069  8.42575069]
+        #    [ 6.88975     7.07525     7.40625     7.85725     8.342
+        #      8.658       9.14275     9.59375     9.92475    10.11025   ]
+        #    [ 8.57424931  8.75974931  9.09074931  9.54174931 10.02649931
+        #     10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
+        #    [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
+        #     12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
+        #    [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
+        #     14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
+        #    [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
+        #     15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_cubic",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_cubic() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="cubic",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 1.63078704  3.00462963  4.37847222]
+        #    [ 7.12615741  8.5         9.87384259]
+        #    [12.62152778 13.99537037 15.36921296]]]]
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_cubic",
+        )
+
+    # TensorFlow v1 bicubic with half_pixel_centers=True
+    @staticmethod
+    def export_resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            cubic_coeff_a=-0.5,
+            exclude_outside=True,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 0.55882353  0.81494204  1.35698249  1.89705882  2.39705882
+        #      2.93713516  3.47917561  3.73529412]
+        #    [ 1.58329755  1.83941606  2.38145651  2.92153285  3.42153285
+        #      3.96160918  4.50364964  4.75976814]
+        #    [ 3.75145936  4.00757787  4.54961832  5.08969466  5.58969466
+        #      6.12977099  6.67181144  6.92792995]
+        #    [ 5.91176471  6.16788321  6.70992366  7.25        7.75
+        #      8.29007634  8.83211679  9.08823529]
+        #    [ 7.91176471  8.16788321  8.70992366  9.25        9.75
+        #     10.29007634 10.83211679 11.08823529]
+        #    [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
+        #     12.45038168 12.99242213 13.24854064]
+        #    [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
+        #     14.61854349 15.16058394 15.41670245]
+        #    [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
+        #     15.64301751 16.18505796 16.44117647]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.5),
+            scale_factors=scales,
+            exclude_outside=True,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            cubic_coeff_a=-0.5,
+            exclude_outside=True,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        # [[[[ 1.36812675  2.6695014   4.0133367 ]
+        #    [ 6.57362535  7.875       9.2188353 ]
+        #    [11.94896657 13.25034122 14.59417652]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.5),
+            scale_factors=scales,
+            exclude_outside=True,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside",
+        )
+
+    # TensorFlow v1 bicubic with half_pixel_centers=False
+    @staticmethod
+    def export_resize_upsample_scales_cubic_asymmetric() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            coordinate_transformation_mode="asymmetric",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        # [[[[ 1.       1.40625  2.       2.5      3.       3.59375  4.
+        #      4.09375]
+        #    [ 2.625    3.03125  3.625    4.125    4.625    5.21875  5.625
+        #      5.71875]
+        #    [ 5.       5.40625  6.       6.5      7.       7.59375  8.
+        #      8.09375]
+        #    [ 7.       7.40625  8.       8.5      9.       9.59375 10.
+        #     10.09375]
+        #    [ 9.       9.40625 10.      10.5     11.      11.59375 12.
+        #     12.09375]
+        #    [11.375   11.78125 12.375   12.875   13.375   13.96875 14.375
+        #     14.46875]
+        #    [13.      13.40625 14.      14.5     15.      15.59375 16.
+        #     16.09375]
+        #    [13.375   13.78125 14.375   14.875   15.375   15.96875 16.375
+        #     16.46875]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.75),
+            scale_factors=scales,
+            coordinate_transformation_mode="asymmetric",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_cubic_asymmetric",
+        )
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "roi", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="tf_crop_and_resize",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004  7.9        8.2      ]
+        #    [ 8.8        9.1        9.400001 ]
+        #    [10.        10.3       10.6      ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, roi, sizes],
+            outputs=[output],
+            name="test_resize_tf_crop_and_resize",
+        )
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize_extrapolation_value() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "roi", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="tf_crop_and_resize",
+            extrapolation_value=10.0,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004 10.        10.       ]
+        #    [12.400001  10.        10.       ]
+        #    [10.        10.        10.       ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            coordinate_transformation_mode="tf_crop_and_resize",
+            extrapolation_value=10.0,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, roi, sizes],
+            outputs=[output],
+            name="test_resize_tf_crop_and_resize_extrapolation_value",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="pytorch_half_pixel",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 1], dtype=np.int64)
+
+        # [[[[ 1.6666666]
+        #    [ 7.       ]
+        #    [12.333333 ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            coordinate_transformation_mode="pytorch_half_pixel",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_linear_pytorch_half_pixel",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_floor_align_corners() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            coordinate_transformation_mode="align_corners",
+            nearest_mode="floor",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 1.  1.  1.  2.  2.  3.  3.  4.]
+        #    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+        #    [ 5.  5.  5.  6.  6.  7.  7.  8.]
+        #    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+        #    [ 9.  9.  9. 10. 10. 11. 11. 12.]
+        #    [13. 13. 13. 14. 14. 15. 15. 16.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x, mode="floor"),
+            output_size=sizes,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_floor_align_corners",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            coordinate_transformation_mode="asymmetric",
+            nearest_mode="round_prefer_ceil",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"),
+            output_size=sizes,
+            coordinate_transformation_mode="asymmetric",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_ceil_half_pixel() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            coordinate_transformation_mode="half_pixel",
+            nearest_mode="ceil",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        # [[[[ 1.  2.  2.  3.  3.  4.  4.  4.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 5.  6.  6.  7.  7.  8.  8.  8.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [ 9. 10. 10. 11. 11. 12. 12. 12.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]
+        #    [13. 14. 14. 15. 15. 16. 16. 16.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_ceil_half_pixel",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_linear_antialias() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+            antialias=1,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[ 2.875  4.5  ]
+        #    [ 9.375 11.   ]]]]
+        output = interpolate_nd(
+            data, linear_coeffs_antialias, scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_linear_antialias",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_linear_antialias() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            antialias=1,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 2.3636363  3.590909   4.818182 ]
+        #    [ 7.2727275  8.5        9.727273 ]
+        #    [12.181818  13.409091  14.636364 ]]]]
+        output = interpolate_nd(
+            data, linear_coeffs_antialias, output_size=sizes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_linear_antialias",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_cubic_antialias() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="cubic",
+            antialias=1,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        # [[[[ 2.5180721  4.2858863]
+        #    [ 9.589329  11.357142 ]]]]
+        output = interpolate_nd(
+            data, cubic_coeffs_antialias, scale_factors=scales
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_cubic_antialias",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_cubic_antialias() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="cubic",
+            antialias=1,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        # [[[[ 1.7750092  3.1200073  4.4650054]
+        #    [ 7.1550016  8.5        9.844998 ]
+        #    [12.534994  13.8799925 15.224991 ]]]]
+        output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype(
+            np.float32
+        )
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_cubic_antialias",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_nearest_axes_2_3() -> None:
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([2.0, 3.0], dtype=np.float32)
+
+        # [[[[1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 2. 2. 2.]
+        #    [3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_nearest_axes_2_3",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_nearest_axes_3_2() -> None:
+        axes = [3, 2]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([3.0, 2.0], dtype=np.float32)
+
+        # [[[[1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 2. 2. 2.]
+        #    [3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_nearest_axes_3_2",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_axes_2_3() -> None:
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64)
+
+        # [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_axes_2_3",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_axes_3_2() -> None:
+        axes = [3, 2]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([8, 7], dtype=np.int64)
+
+        # [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_axes_3_2",
+        )
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize_axes_2_3() -> None:
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "roi", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="tf_crop_and_resize",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32)
+        sizes = np.array([3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004  7.9        8.2      ]
+        #    [ 8.8        9.1        9.400001 ]
+        #    [10.        10.3       10.6      ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            axes=axes,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, roi, sizes],
+            outputs=[output],
+            name="test_resize_tf_crop_and_resize_axes_2_3",
+        )
+
+    @staticmethod
+    def export_resize_tf_crop_and_resize_axes_3_2() -> None:
+        axes = [3, 2]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "roi", "", "sizes"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="tf_crop_and_resize",
+            axes=axes,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        # Note: for some rois, the result may be different with that of TF for inaccurate floating point
+        roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
+        sizes = np.array([3, 3], dtype=np.int64)
+
+        # [[[[ 7.6000004  7.9        8.2      ]
+        #    [ 8.8        9.1        9.400001 ]
+        #    [10.        10.3       10.6      ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            axes=axes,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, roi, sizes],
+            outputs=[output],
+            name="test_resize_tf_crop_and_resize_axes_3_2",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_not_larger() -> None:
+        keep_aspect_ratio_policy = "not_larger"
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64)  # Results in 7x7
+
+        # [[[[1. 1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_not_larger",
+        )
+
+    @staticmethod
+    def export_resize_upsample_sizes_nearest_not_smaller() -> None:
+        keep_aspect_ratio_policy = "not_smaller"
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64)  # Results in 8x8
+
+        # [[[[1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [1. 1. 1. 1. 2. 2. 2. 2.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]
+        #    [3. 3. 3. 3. 4. 4. 4. 4.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_upsample_sizes_nearest_not_smaller",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_nearest_not_larger() -> None:
+        keep_aspect_ratio_policy = "not_larger"
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 3], dtype=np.int64)  # Results in 1x2
+
+        # [[[[1. 3.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_nearest_not_larger",
+        )
+
+    @staticmethod
+    def export_resize_downsample_sizes_nearest_not_smaller() -> None:
+        keep_aspect_ratio_policy = "not_smaller"
+        axes = [2, 3]
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "", "sizes"],
+            outputs=["Y"],
+            mode="nearest",
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 3], dtype=np.int64)  # Results in 2x3
+
+        # [[[[1. 2. 4.]
+        #    [5. 6. 8.]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, sizes],
+            outputs=[output],
+            name="test_resize_downsample_sizes_nearest_not_smaller",
+        )
+
+    @staticmethod
+    def export_resize_downsample_scales_linear_half_pixel_symmetric() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="half_pixel_symmetric",
+        )
+
+        data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32)
+        scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32)
+
+        # [[[[1.6666667, 3.3333333]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="half_pixel_symmetric",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_downsample_scales_linear_half_pixel_symmetric",
+        )
+
+    @staticmethod
+    def export_resize_upsample_scales_linear_half_pixel_symmetric() -> None:
+        node = onnx.helper.make_node(
+            "Resize",
+            inputs=["X", "", "scales"],
+            outputs=["Y"],
+            mode="linear",
+            coordinate_transformation_mode="half_pixel_symmetric",
+        )
+
+        data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
+        scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32)
+
+        # [[[[1.        , 1.15986395, 1.5       , 1.84013605, 2.        ],
+        #    [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738],
+        #    [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262],
+        #    [3.        , 3.15986395, 3.5       , 3.84013605, 4.        ]]]]
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="half_pixel_symmetric",
+        ).astype(np.float32)
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_resize_upsample_scales_linear_half_pixel_symmetric",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reversesequence.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reversesequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..fba86660d48973782bcdace71a859dbf294553d0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/reversesequence.py
@@ -0,0 +1,86 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ReverseSequence(Base):
+    @staticmethod
+    def export_reversesequence_time() -> None:
+        node = onnx.helper.make_node(
+            "ReverseSequence",
+            inputs=["x", "sequence_lens"],
+            outputs=["y"],
+            time_axis=0,
+            batch_axis=1,
+        )
+        x = np.array(
+            [
+                [0.0, 4.0, 8.0, 12.0],
+                [1.0, 5.0, 9.0, 13.0],
+                [2.0, 6.0, 10.0, 14.0],
+                [3.0, 7.0, 11.0, 15.0],
+            ],
+            dtype=np.float32,
+        )
+        sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
+
+        y = np.array(
+            [
+                [3.0, 6.0, 9.0, 12.0],
+                [2.0, 5.0, 8.0, 13.0],
+                [1.0, 4.0, 10.0, 14.0],
+                [0.0, 7.0, 11.0, 15.0],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[x, sequence_lens],
+            outputs=[y],
+            name="test_reversesequence_time",
+        )
+
+    @staticmethod
+    def export_reversesequence_batch() -> None:
+        node = onnx.helper.make_node(
+            "ReverseSequence",
+            inputs=["x", "sequence_lens"],
+            outputs=["y"],
+            time_axis=1,
+            batch_axis=0,
+        )
+        x = np.array(
+            [
+                [0.0, 1.0, 2.0, 3.0],
+                [4.0, 5.0, 6.0, 7.0],
+                [8.0, 9.0, 10.0, 11.0],
+                [12.0, 13.0, 14.0, 15.0],
+            ],
+            dtype=np.float32,
+        )
+        sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
+
+        y = np.array(
+            [
+                [0.0, 1.0, 2.0, 3.0],
+                [5.0, 4.0, 6.0, 7.0],
+                [10.0, 9.0, 8.0, 11.0],
+                [15.0, 14.0, 13.0, 12.0],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[x, sequence_lens],
+            outputs=[y],
+            name="test_reversesequence_batch",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rmsnormalization.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rmsnormalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5415158e278691bff2b7d36e8e52e2cdb4785bfa
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rmsnormalization.py
@@ -0,0 +1,126 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_rms_normalization import _rms_normalization
+
+
+def calculate_normalized_shape(x_shape, axis):
+    rank = len(x_shape)
+    if axis < 0:
+        axis = axis + rank
+    return x_shape[axis:]
+
+
+class RMSNormalization(Base):
+    @staticmethod
+    def export() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            Y = _rms_normalization(X, W, axis=axis)
+
+            node = onnx.helper.make_node(
+                "RMSNormalization",
+                inputs=["X", "W"],
+                outputs=["Y"],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f"test_rms_normalization_4d_axis_negative_{-axis}"
+            else:
+                name = f"test_rms_normalization_4d_axis{axis}"
+
+            expect(node, inputs=[X, W], outputs=[Y], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export_default_axis() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        # Default axis in RMSNormalization is -1.
+        normalized_shape = calculate_normalized_shape(X.shape, -1)
+        W = np.random.randn(*normalized_shape).astype(np.float32)
+        # Axis is default to -1 in the reference implementation.
+        Y = _rms_normalization(X, W)
+
+        # Not specifying axis attribute means -1.
+        node = onnx.helper.make_node(
+            "RMSNormalization",
+            inputs=["X", "W"],
+            outputs=["Y"],
+        )
+
+        expect(
+            node,
+            inputs=[X, W],
+            outputs=[Y],
+            name="test_rms_normalization_default_axis",
+        )
+
+    @staticmethod
+    def export2d() -> None:
+        X = np.random.randn(3, 4).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            Y = _rms_normalization(X, W, axis=axis)
+
+            node = onnx.helper.make_node(
+                "RMSNormalization",
+                inputs=["X", "W"],
+                outputs=["Y"],
+                axis=axis,
+            )
+
+            if axis < 0:
+                name = f"test_rms_normalization_2d_axis_negative_{-axis}"
+            else:
+                name = f"test_rms_normalization_2d_axis{axis}"
+
+            expect(node, inputs=[X, W], outputs=[Y], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export3d_epsilon() -> None:
+        epsilon = 1e-1
+        X = np.random.randn(2, 3, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            Y = _rms_normalization(X, W, axis=axis, epsilon=epsilon)
+            node = onnx.helper.make_node(
+                "RMSNormalization",
+                inputs=["X", "W"],
+                outputs=["Y"],
+                axis=axis,
+                epsilon=epsilon,
+            )
+
+            if axis < 0:
+                name = f"test_rms_normalization_3d_axis_negative_{-axis}_epsilon"
+            else:
+                name = f"test_rms_normalization_3d_axis{axis}_epsilon"
+
+            expect(node, inputs=[X, W], outputs=[Y], name=name)
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rnn.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..983f37dc6eb5f56aad41a77b5dc8f5fc649f56f0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rnn.py
@@ -0,0 +1,220 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class RNNHelper:
+    def __init__(self, **params: Any) -> None:
+        # RNN Input Names
+        X = "X"
+        W = "W"
+        R = "R"
+        B = "B"
+        H_0 = "initial_h"
+        LAYOUT = "layout"
+
+        required_inputs = [X, W, R]
+        for i in required_inputs:
+            assert i in params, f"Missing Required Input: {i}"
+
+        self.num_directions = params[str(W)].shape[0]
+
+        if self.num_directions == 1:
+            for k, v in params.items():
+                if k != X:
+                    params[k] = np.squeeze(v, axis=0)
+
+            hidden_size = params[R].shape[-1]
+            batch_size = params[X].shape[1]
+
+            layout = params.get(LAYOUT, 0)
+            x = params[X]
+            x = x if layout == 0 else np.swapaxes(x, 0, 1)
+            b = (
+                params[B]
+                if B in params
+                else np.zeros(2 * hidden_size, dtype=np.float32)
+            )
+            h_0 = (
+                params[H_0]
+                if H_0 in params
+                else np.zeros((batch_size, hidden_size), dtype=np.float32)
+            )
+
+            self.X = x
+            self.W = params[W]
+            self.R = params[R]
+            self.B = b
+            self.H_0 = h_0
+            self.LAYOUT = layout
+
+        else:
+            raise NotImplementedError()
+
+    def f(self, x: np.ndarray) -> np.ndarray:
+        return np.tanh(x)
+
+    def step(self) -> tuple[np.ndarray, np.ndarray]:
+        seq_length = self.X.shape[0]
+        hidden_size = self.H_0.shape[-1]
+        batch_size = self.X.shape[1]
+
+        Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
+        h_list = []
+
+        H_t = self.H_0
+        for x in np.split(self.X, self.X.shape[0], axis=0):
+            H = self.f(
+                np.dot(x, np.transpose(self.W))
+                + np.dot(H_t, np.transpose(self.R))
+                + np.add(*np.split(self.B, 2))
+            )
+            h_list.append(H)
+            H_t = H
+
+        concatenated = np.concatenate(h_list)
+        if self.num_directions == 1:
+            Y[:, 0, :, :] = concatenated
+
+        if self.LAYOUT == 0:
+            Y_h = Y[-1]
+        else:
+            Y = np.transpose(Y, [2, 0, 1, 3])
+            Y_h = Y[:, :, -1, :]
+
+        return Y, Y_h
+
+
+class RNN(Base):
+    @staticmethod
+    def export_defaults() -> None:
+        input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 4
+        weight_scale = 0.1
+
+        node = onnx.helper.make_node(
+            "RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        rnn = RNNHelper(X=input, W=W, R=R)
+        _, Y_h = rnn.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_simple_rnn_defaults",
+        )
+
+    @staticmethod
+    def export_initial_bias() -> None:
+        input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
+            np.float32
+        )
+
+        input_size = 3
+        hidden_size = 5
+        custom_bias = 0.1
+        weight_scale = 0.1
+
+        node = onnx.helper.make_node(
+            "RNN",
+            inputs=["X", "W", "R", "B"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        # Adding custom bias
+        W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
+        R_B = np.zeros((1, hidden_size)).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        rnn = RNNHelper(X=input, W=W, R=R, B=B)
+        _, Y_h = rnn.step()
+        expect(
+            node,
+            inputs=[input, W, R, B],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_simple_rnn_with_initial_bias",
+        )
+
+    @staticmethod
+    def export_seq_length() -> None:
+        input = np.array(
+            [
+                [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
+                [[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
+            ]
+        ).astype(np.float32)
+
+        input_size = 3
+        hidden_size = 5
+
+        node = onnx.helper.make_node(
+            "RNN",
+            inputs=["X", "W", "R", "B"],
+            outputs=["", "Y_h"],
+            hidden_size=hidden_size,
+        )
+
+        W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
+        R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
+
+        # Adding custom bias
+        W_B = np.random.randn(1, hidden_size).astype(np.float32)
+        R_B = np.random.randn(1, hidden_size).astype(np.float32)
+        B = np.concatenate((W_B, R_B), axis=1)
+
+        rnn = RNNHelper(X=input, W=W, R=R, B=B)
+        _, Y_h = rnn.step()
+        expect(
+            node,
+            inputs=[input, W, R, B],
+            outputs=[Y_h.astype(np.float32)],
+            name="test_rnn_seq_length",
+        )
+
+    @staticmethod
+    def export_batchwise() -> None:
+        input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
+
+        input_size = 2
+        hidden_size = 4
+        weight_scale = 0.5
+        layout = 1
+
+        node = onnx.helper.make_node(
+            "RNN",
+            inputs=["X", "W", "R"],
+            outputs=["Y", "Y_h"],
+            hidden_size=hidden_size,
+            layout=layout,
+        )
+
+        W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
+        R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
+
+        rnn = RNNHelper(X=input, W=W, R=R, layout=layout)
+        Y, Y_h = rnn.step()
+        expect(
+            node,
+            inputs=[input, W, R],
+            outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
+            name="test_simple_rnn_batchwise",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/roialign.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/roialign.py
new file mode 100644
index 0000000000000000000000000000000000000000..b59ef06996c63a8cb46e3ce638fa75ac17c4fdff
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/roialign.py
@@ -0,0 +1,446 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def get_roi_align_input_values():
+    X = np.array(
+        [
+            [
+                [
+                    [
+                        0.2764,
+                        0.7150,
+                        0.1958,
+                        0.3416,
+                        0.4638,
+                        0.0259,
+                        0.2963,
+                        0.6518,
+                        0.4856,
+                        0.7250,
+                    ],
+                    [
+                        0.9637,
+                        0.0895,
+                        0.2919,
+                        0.6753,
+                        0.0234,
+                        0.6132,
+                        0.8085,
+                        0.5324,
+                        0.8992,
+                        0.4467,
+                    ],
+                    [
+                        0.3265,
+                        0.8479,
+                        0.9698,
+                        0.2471,
+                        0.9336,
+                        0.1878,
+                        0.4766,
+                        0.4308,
+                        0.3400,
+                        0.2162,
+                    ],
+                    [
+                        0.0206,
+                        0.1720,
+                        0.2155,
+                        0.4394,
+                        0.0653,
+                        0.3406,
+                        0.7724,
+                        0.3921,
+                        0.2541,
+                        0.5799,
+                    ],
+                    [
+                        0.4062,
+                        0.2194,
+                        0.4473,
+                        0.4687,
+                        0.7109,
+                        0.9327,
+                        0.9815,
+                        0.6320,
+                        0.1728,
+                        0.6119,
+                    ],
+                    [
+                        0.3097,
+                        0.1283,
+                        0.4984,
+                        0.5068,
+                        0.4279,
+                        0.0173,
+                        0.4388,
+                        0.0430,
+                        0.4671,
+                        0.7119,
+                    ],
+                    [
+                        0.1011,
+                        0.8477,
+                        0.4726,
+                        0.1777,
+                        0.9923,
+                        0.4042,
+                        0.1869,
+                        0.7795,
+                        0.9946,
+                        0.9689,
+                    ],
+                    [
+                        0.1366,
+                        0.3671,
+                        0.7011,
+                        0.6234,
+                        0.9867,
+                        0.5585,
+                        0.6985,
+                        0.5609,
+                        0.8788,
+                        0.9928,
+                    ],
+                    [
+                        0.5697,
+                        0.8511,
+                        0.6711,
+                        0.9406,
+                        0.8751,
+                        0.7496,
+                        0.1650,
+                        0.1049,
+                        0.1559,
+                        0.2514,
+                    ],
+                    [
+                        0.7012,
+                        0.4056,
+                        0.7879,
+                        0.3461,
+                        0.0415,
+                        0.2998,
+                        0.5094,
+                        0.3727,
+                        0.5482,
+                        0.0502,
+                    ],
+                ]
+            ]
+        ],
+        dtype=np.float32,
+    )
+    batch_indices = np.array([0, 0, 0], dtype=np.int64)
+    rois = np.array([[0, 0, 9, 9], [0, 5, 4, 9], [5, 5, 9, 9]], dtype=np.float32)
+    return X, batch_indices, rois
+
+
+class RoiAlign(Base):
+    @staticmethod
+    def export_roialign_aligned_false() -> None:
+        node = onnx.helper.make_node(
+            "RoiAlign",
+            inputs=["X", "rois", "batch_indices"],
+            outputs=["Y"],
+            spatial_scale=1.0,
+            output_height=5,
+            output_width=5,
+            sampling_ratio=2,
+            coordinate_transformation_mode="output_half_pixel",
+        )
+
+        X, batch_indices, rois = get_roi_align_input_values()
+        # (num_rois, C, output_height, output_width)
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.4664, 0.4466, 0.3405, 0.5688, 0.6068],
+                        [0.3714, 0.4296, 0.3835, 0.5562, 0.3510],
+                        [0.2768, 0.4883, 0.5222, 0.5528, 0.4171],
+                        [0.4713, 0.4844, 0.6904, 0.4920, 0.8774],
+                        [0.6239, 0.7125, 0.6289, 0.3355, 0.3495],
+                    ]
+                ],
+                [
+                    [
+                        [0.3022, 0.4305, 0.4696, 0.3978, 0.5423],
+                        [0.3656, 0.7050, 0.5165, 0.3172, 0.7015],
+                        [0.2912, 0.5059, 0.6476, 0.6235, 0.8299],
+                        [0.5916, 0.7389, 0.7048, 0.8372, 0.8893],
+                        [0.6227, 0.6153, 0.7097, 0.6154, 0.4585],
+                    ]
+                ],
+                [
+                    [
+                        [0.2384, 0.3379, 0.3717, 0.6100, 0.7601],
+                        [0.3767, 0.3785, 0.7147, 0.9243, 0.9727],
+                        [0.5749, 0.5826, 0.5709, 0.7619, 0.8770],
+                        [0.5355, 0.2566, 0.2141, 0.2796, 0.3600],
+                        [0.4365, 0.3504, 0.2887, 0.3661, 0.2349],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, rois, batch_indices],
+            outputs=[Y],
+            name="test_roialign_aligned_false",
+        )
+
+    @staticmethod
+    def export_roialign_aligned_true() -> None:
+        node = onnx.helper.make_node(
+            "RoiAlign",
+            inputs=["X", "rois", "batch_indices"],
+            outputs=["Y"],
+            spatial_scale=1.0,
+            output_height=5,
+            output_width=5,
+            sampling_ratio=2,
+            coordinate_transformation_mode="half_pixel",
+        )
+
+        X, batch_indices, rois = get_roi_align_input_values()
+        # (num_rois, C, output_height, output_width)
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.5178, 0.3434, 0.3229, 0.4474, 0.6344],
+                        [0.4031, 0.5366, 0.4428, 0.4861, 0.4023],
+                        [0.2512, 0.4002, 0.5155, 0.6954, 0.3465],
+                        [0.3350, 0.4601, 0.5881, 0.3439, 0.6849],
+                        [0.4932, 0.7141, 0.8217, 0.4719, 0.4039],
+                    ]
+                ],
+                [
+                    [
+                        [0.3070, 0.2187, 0.3337, 0.4880, 0.4870],
+                        [0.1871, 0.4914, 0.5561, 0.4192, 0.3686],
+                        [0.1433, 0.4608, 0.5971, 0.5310, 0.4982],
+                        [0.2788, 0.4386, 0.6022, 0.7000, 0.7524],
+                        [0.5774, 0.7024, 0.7251, 0.7338, 0.8163],
+                    ]
+                ],
+                [
+                    [
+                        [0.2393, 0.4075, 0.3379, 0.2525, 0.4743],
+                        [0.3671, 0.2702, 0.4105, 0.6419, 0.8308],
+                        [0.5556, 0.4543, 0.5564, 0.7502, 0.9300],
+                        [0.6626, 0.5617, 0.4813, 0.4954, 0.6663],
+                        [0.6636, 0.3721, 0.2056, 0.1928, 0.2478],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[X, rois, batch_indices],
+            outputs=[Y],
+            name="test_roialign_aligned_true",
+        )
+
+    @staticmethod
+    def export_roialign_mode_max() -> None:
+        X = np.array(
+            [
+                [
+                    [
+                        [
+                            0.2764,
+                            0.715,
+                            0.1958,
+                            0.3416,
+                            0.4638,
+                            0.0259,
+                            0.2963,
+                            0.6518,
+                            0.4856,
+                            0.725,
+                        ],
+                        [
+                            0.9637,
+                            0.0895,
+                            0.2919,
+                            0.6753,
+                            0.0234,
+                            0.6132,
+                            0.8085,
+                            0.5324,
+                            0.8992,
+                            0.4467,
+                        ],
+                        [
+                            0.3265,
+                            0.8479,
+                            0.9698,
+                            0.2471,
+                            0.9336,
+                            0.1878,
+                            0.4766,
+                            0.4308,
+                            0.34,
+                            0.2162,
+                        ],
+                        [
+                            0.0206,
+                            0.172,
+                            0.2155,
+                            0.4394,
+                            0.0653,
+                            0.3406,
+                            0.7724,
+                            0.3921,
+                            0.2541,
+                            0.5799,
+                        ],
+                        [
+                            0.4062,
+                            0.2194,
+                            0.4473,
+                            0.4687,
+                            0.7109,
+                            0.9327,
+                            0.9815,
+                            0.632,
+                            0.1728,
+                            0.6119,
+                        ],
+                        [
+                            0.3097,
+                            0.1283,
+                            0.4984,
+                            0.5068,
+                            0.4279,
+                            0.0173,
+                            0.4388,
+                            0.043,
+                            0.4671,
+                            0.7119,
+                        ],
+                        [
+                            0.1011,
+                            0.8477,
+                            0.4726,
+                            0.1777,
+                            0.9923,
+                            0.4042,
+                            0.1869,
+                            0.7795,
+                            0.9946,
+                            0.9689,
+                        ],
+                        [
+                            0.1366,
+                            0.3671,
+                            0.7011,
+                            0.6234,
+                            0.9867,
+                            0.5585,
+                            0.6985,
+                            0.5609,
+                            0.8788,
+                            0.9928,
+                        ],
+                        [
+                            0.5697,
+                            0.8511,
+                            0.6711,
+                            0.9406,
+                            0.8751,
+                            0.7496,
+                            0.165,
+                            0.1049,
+                            0.1559,
+                            0.2514,
+                        ],
+                        [
+                            0.7012,
+                            0.4056,
+                            0.7879,
+                            0.3461,
+                            0.0415,
+                            0.2998,
+                            0.5094,
+                            0.3727,
+                            0.5482,
+                            0.0502,
+                        ],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        rois = np.array(
+            [[0.0, 0.0, 9.0, 9.0], [0.0, 5.0, 4.0, 9.0], [5.0, 5.0, 9.0, 9.0]],
+            dtype=np.float32,
+        )
+        batch_indices = np.array([0, 0, 0], dtype=np.int64)
+
+        Y = np.array(
+            [
+                [
+                    [
+                        [0.3445228, 0.37310338, 0.37865096, 0.446696, 0.37991184],
+                        [0.4133513, 0.5455125, 0.6651902, 0.55805874, 0.27110294],
+                        [0.21223956, 0.40924096, 0.8417618, 0.792561, 0.37196714],
+                        [0.46835402, 0.39741728, 0.8012819, 0.4969306, 0.5495158],
+                        [0.3595896, 0.5196813, 0.5403741, 0.23814403, 0.19992709],
+                    ]
+                ],
+                [
+                    [
+                        [0.30517197, 0.5086199, 0.3189761, 0.4054401, 0.47630402],
+                        [0.50862, 0.8477, 0.37808004, 0.24936005, 0.79384017],
+                        [0.17620805, 0.29368007, 0.44870415, 0.4987201, 0.63148826],
+                        [0.51066005, 0.8511, 0.5368801, 0.9406, 0.70008016],
+                        [0.4487681, 0.51066035, 0.5042561, 0.5643603, 0.42004836],
+                    ]
+                ],
+                [
+                    [
+                        [0.21062402, 0.3510401, 0.37416005, 0.5967599, 0.46507207],
+                        [0.32336006, 0.31180006, 0.6236001, 0.9946, 0.7751202],
+                        [0.35744014, 0.5588001, 0.35897616, 0.7030401, 0.6353923],
+                        [0.5996801, 0.27940005, 0.17948808, 0.35152006, 0.31769615],
+                        [0.3598083, 0.40752012, 0.2385281, 0.43856013, 0.26313624],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+
+        node = onnx.helper.make_node(
+            "RoiAlign",
+            inputs=["X", "rois", "batch_indices"],
+            mode="max",
+            outputs=["Y"],
+            spatial_scale=1.0,
+            output_height=5,
+            output_width=5,
+            sampling_ratio=2,
+            coordinate_transformation_mode="output_half_pixel",
+        )
+
+        expect(
+            node,
+            inputs=[X, rois, batch_indices],
+            outputs=[Y],
+            name="test_roialign_mode_max",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rotaryembedding.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rotaryembedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..3852d6303e336fb7f5723ab7d60ce0b7cfb10284
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/rotaryembedding.py
@@ -0,0 +1,231 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+from onnx.reference.ops.op_rotary_embedding import rotary_embedding
+
+
+class RotaryEmbedding(Base):
+    @staticmethod
+    def export_rotary_embedding() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache", "position_ids"],
+            outputs=["output"],
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64)
+        sin_cache_data = np.random.rand(50, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(50, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data, cos_cache_data, sin_cache_data, position_ids=position_ids_data
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_3d_input() -> None:
+        num_heads = 4
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache", "position_ids"],
+            outputs=["output"],
+            num_heads=num_heads,
+        )
+
+        input_data = np.random.rand(2, 3, 32).astype(np.float32)
+        position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64)
+        sin_cache_data = np.random.rand(50, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(50, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            position_ids=position_ids_data,
+            num_heads=num_heads,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_3d_input",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_interleaved() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache", "position_ids"],
+            outputs=["output"],
+            interleaved=1,
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64)
+        sin_cache_data = np.random.rand(50, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(50, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            position_ids=position_ids_data,
+            interleaved=1,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_interleaved",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_with_rotary_dim() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache", "position_ids"],
+            outputs=["output"],
+            rotary_embedding_dim=4,
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64)
+        sin_cache_data = np.random.rand(50, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(50, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            position_ids=position_ids_data,
+            rotary_embedding_dim=4,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_with_rotary_dim",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_with_interleaved_rotary_dim() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache", "position_ids"],
+            outputs=["output"],
+            rotary_embedding_dim=4,
+            interleaved=1,
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        position_ids_data = np.random.uniform(0, 50, (2, 3)).astype(np.int64)
+        sin_cache_data = np.random.rand(50, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(50, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            position_ids=position_ids_data,
+            interleaved=1,
+            rotary_embedding_dim=4,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data, position_ids_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_with_interleaved_rotary_dim",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_no_position_ids() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache"],
+            outputs=["output"],
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(input_data, cos_cache_data, sin_cache_data)
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_no_position_ids",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_no_position_ids_interleaved() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache"],
+            outputs=["output"],
+            interleaved=1,
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            interleaved=1,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_no_position_ids_interleaved",
+        )
+
+    @staticmethod
+    def export_rotary_embedding_no_position_ids_rotary_dim() -> None:
+        node = onnx.helper.make_node(
+            "RotaryEmbedding",
+            inputs=["input", "cos_cache", "sin_cache"],
+            outputs=["output"],
+            rotary_embedding_dim=4,
+        )
+
+        input_data = np.random.rand(2, 4, 3, 8).astype(np.float32)
+        sin_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+        cos_cache_data = np.random.rand(2, 3, 4).astype(np.float32)
+
+        expected_output = rotary_embedding(
+            input_data,
+            cos_cache_data,
+            sin_cache_data,
+            rotary_embedding_dim=4,
+        )
+
+        expect(
+            node,
+            inputs=[input_data, cos_cache_data, sin_cache_data],
+            outputs=[expected_output],
+            name="test_rotary_embedding_no_position_ids_rotary_dim",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/round.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/round.py
new file mode 100644
index 0000000000000000000000000000000000000000..6924285eeab71c3cee7bbb4cb4c49d8808df3df9
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/round.py
@@ -0,0 +1,62 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Round(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Round",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                0.1,
+                0.5,
+                0.9,
+                1.2,
+                1.5,
+                1.8,
+                2.3,
+                2.5,
+                2.7,
+                -1.1,
+                -1.5,
+                -1.9,
+                -2.2,
+                -2.5,
+                -2.8,
+            ]
+        ).astype(np.float32)
+
+        # expected output
+        y = np.array(
+            [
+                0.0,
+                0.0,
+                1.0,
+                1.0,
+                2.0,
+                2.0,
+                2.0,
+                2.0,
+                3.0,
+                -1.0,
+                -2.0,
+                -2.0,
+                -2.0,
+                -2.0,
+                -3.0,
+            ]
+        ).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_round")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scan.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scan.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f251446787f1799bb7dac46c6b9d47f9f44ee7f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scan.py
@@ -0,0 +1,117 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Scan(Base):
+    @staticmethod
+    def export_scan_8() -> None:
+        # Given an input sequence [x1, ..., xN], sum up its elements using a scan
+        # returning the final state (x1+x2+...+xN) as well the scan_output
+        # [x1, x1+x2, ..., x1+x2+...+xN]
+        #
+        # create graph to represent scan body
+        sum_in = onnx.helper.make_tensor_value_info(
+            "sum_in", onnx.TensorProto.FLOAT, [2]
+        )
+        next = onnx.helper.make_tensor_value_info(  # noqa: A001
+            "next", onnx.TensorProto.FLOAT, [2]
+        )
+        sum_out = onnx.helper.make_tensor_value_info(
+            "sum_out", onnx.TensorProto.FLOAT, [2]
+        )
+        scan_out = onnx.helper.make_tensor_value_info(
+            "scan_out", onnx.TensorProto.FLOAT, [2]
+        )
+        add_node = onnx.helper.make_node(
+            "Add", inputs=["sum_in", "next"], outputs=["sum_out"]
+        )
+        id_node = onnx.helper.make_node(
+            "Identity", inputs=["sum_out"], outputs=["scan_out"]
+        )
+        scan_body = onnx.helper.make_graph(
+            [add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
+        )
+        # create scan op node
+        no_sequence_lens = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "Scan",
+            inputs=[no_sequence_lens, "initial", "x"],
+            outputs=["y", "z"],
+            num_scan_inputs=1,
+            body=scan_body,
+        )
+        # create inputs for batch-size 1, sequence-length 3, inner dimension 2
+        initial = np.array([0, 0]).astype(np.float32).reshape((1, 2))
+        x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((1, 3, 2))
+        # final state computed = [1 + 3 + 5, 2 + 4 + 6]
+        y = np.array([9, 12]).astype(np.float32).reshape((1, 2))
+        # scan-output computed
+        z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((1, 3, 2))
+
+        expect(
+            node,
+            inputs=[initial, x],
+            outputs=[y, z],
+            name="test_scan_sum",
+            opset_imports=[onnx.helper.make_opsetid("", 8)],
+        )
+
+    @staticmethod
+    def export_scan_9() -> None:
+        # Given an input sequence [x1, ..., xN], sum up its elements using a scan
+        # returning the final state (x1+x2+...+xN) as well the scan_output
+        # [x1, x1+x2, ..., x1+x2+...+xN]
+        #
+        # create graph to represent scan body
+        sum_in = onnx.helper.make_tensor_value_info(
+            "sum_in", onnx.TensorProto.FLOAT, [2]
+        )
+        next = onnx.helper.make_tensor_value_info(  # noqa: A001
+            "next", onnx.TensorProto.FLOAT, [2]
+        )
+        sum_out = onnx.helper.make_tensor_value_info(
+            "sum_out", onnx.TensorProto.FLOAT, [2]
+        )
+        scan_out = onnx.helper.make_tensor_value_info(
+            "scan_out", onnx.TensorProto.FLOAT, [2]
+        )
+        add_node = onnx.helper.make_node(
+            "Add", inputs=["sum_in", "next"], outputs=["sum_out"]
+        )
+        id_node = onnx.helper.make_node(
+            "Identity", inputs=["sum_out"], outputs=["scan_out"]
+        )
+        scan_body = onnx.helper.make_graph(
+            [add_node, id_node], "scan_body", [sum_in, next], [sum_out, scan_out]
+        )
+        # create scan op node
+        node = onnx.helper.make_node(
+            "Scan",
+            inputs=["initial", "x"],
+            outputs=["y", "z"],
+            num_scan_inputs=1,
+            body=scan_body,
+        )
+        # create inputs for sequence-length 3, inner dimension 2
+        initial = np.array([0, 0]).astype(np.float32).reshape((2,))
+        x = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32).reshape((3, 2))
+        # final state computed = [1 + 3 + 5, 2 + 4 + 6]
+        y = np.array([9, 12]).astype(np.float32).reshape((2,))
+        # scan-output computed
+        z = np.array([1, 2, 4, 6, 9, 12]).astype(np.float32).reshape((3, 2))
+
+        expect(
+            node,
+            inputs=[initial, x],
+            outputs=[y, z],
+            name="test_scan9_sum",
+            opset_imports=[onnx.helper.make_opsetid("", 9)],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatter.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4ab3c7a3351b6e93db0d6c8d3084ba1ce4345b1
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatter.py
@@ -0,0 +1,105 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx import helper
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# The below Scatter's numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def scatter(data, indices, updates, axis=0):  # type: ignore
+    if axis < 0:
+        axis = data.ndim + axis
+
+    idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
+
+    def make_slice(arr, axis, i):  # type: ignore
+        slc = [slice(None)] * arr.ndim
+        slc[axis] = i
+        return slc
+
+    def unpack(packed):  # type: ignore
+        unpacked = packed[0]
+        for i in range(1, len(packed)):
+            unpacked = unpacked, packed[i]
+        return unpacked
+
+    # We use indices and axis parameters to create idx
+    # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
+    idx = [
+        [
+            unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
+            indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0],
+        ]
+        for i in range(indices.shape[axis])
+    ]
+    idx = list(np.concatenate(idx, axis=1))
+    idx.insert(axis, idx.pop())
+
+    # updates_idx is a NumPy advanced indices for indexing of elements in the updates
+    updates_idx = list(idx)
+    updates_idx.pop(axis)
+    updates_idx.insert(
+        axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape))
+    )
+
+    scattered = np.copy(data)
+    scattered[tuple(idx)] = updates[tuple(updates_idx)]
+    return scattered
+
+
+class Scatter(Base):
+    @staticmethod
+    def export_scatter_without_axis() -> None:
+        node = onnx.helper.make_node(
+            "Scatter",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+        )
+        data = np.zeros((3, 3), dtype=np.float32)
+        indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+        updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+        y = scatter(data, indices, updates)
+        # print(y) produces
+        # [[2.0, 1.1, 0.0],
+        #  [1.0, 0.0, 2.2],
+        #  [0.0, 2.1, 1.2]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_without_axis",
+            opset_imports=[helper.make_opsetid("", 10)],
+        )
+
+    @staticmethod
+    def export_scatter_with_axis() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "Scatter",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter(data, indices, updates, axis=axis)
+        # print(y) produces
+        # [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_with_axis",
+            opset_imports=[helper.make_opsetid("", 10)],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatterelements.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatterelements.py
new file mode 100644
index 0000000000000000000000000000000000000000..d35eaa97761a99be79cd4a1711de5579741cab8c
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatterelements.py
@@ -0,0 +1,228 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# The below ScatterElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
+def scatter_elements(data, indices, updates, axis=0, reduction="none"):  # type: ignore
+    if axis < 0:
+        axis = data.ndim + axis
+
+    idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
+
+    def make_slice(arr, axis, i):  # type: ignore
+        slc = [slice(None)] * arr.ndim
+        slc[axis] = i
+        return slc
+
+    def unpack(packed):  # type: ignore
+        unpacked = packed[0]
+        for i in range(1, len(packed)):
+            unpacked = unpacked, packed[i]
+        return unpacked
+
+    def make_indices_for_duplicate(idx):  # type: ignore
+        final_idx = []
+        for i in range(len(idx[0])):
+            final_idx.append(  # noqa: PERF401
+                tuple(idx_element[i] for idx_element in idx)
+            )
+        return list(final_idx)
+
+    # We use indices and axis parameters to create idx
+    # idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
+    idx = [
+        [
+            unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
+            indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0],
+        ]
+        for i in range(indices.shape[axis])
+    ]
+    idx = list(np.concatenate(idx, axis=1))
+    idx.insert(axis, idx.pop())
+
+    # updates_idx is a NumPy advanced indices for indexing of elements in the updates
+    updates_idx = list(idx)
+    updates_idx.pop(axis)
+    updates_idx.insert(
+        axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape))
+    )
+
+    scattered = np.copy(data)
+    if reduction == "none":
+        scattered[tuple(idx)] = updates[tuple(updates_idx)]
+    else:
+        idx, updates_idx = (
+            make_indices_for_duplicate(idx),
+            make_indices_for_duplicate(updates_idx),
+        )
+        for iter, idx_set in enumerate(idx):  # noqa: A001
+            if reduction == "add":
+                scattered[idx_set] += updates[updates_idx[iter]]
+            elif reduction == "mul":
+                scattered[idx_set] *= updates[updates_idx[iter]]
+            elif reduction == "max":
+                scattered[idx_set] = np.maximum(
+                    scattered[idx_set], updates[updates_idx[iter]]
+                )
+            elif reduction == "min":
+                scattered[idx_set] = np.minimum(
+                    scattered[idx_set], updates[updates_idx[iter]]
+                )
+    return scattered
+
+
+class ScatterElements(Base):
+    @staticmethod
+    def export_scatter_elements_without_axis() -> None:
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+        )
+        data = np.zeros((3, 3), dtype=np.float32)
+        indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
+        updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates)
+        # print(y) produces
+        # [[2.0, 1.1, 0.0],
+        #  [1.0, 0.0, 2.2],
+        #  [0.0, 2.1, 1.2]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_without_axis",
+        )
+
+    @staticmethod
+    def export_scatter_elements_with_axis() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis)
+        # print(y) produces
+        # [[1.0, 1.1, 3.0, 2.1, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_with_axis",
+        )
+
+    @staticmethod
+    def export_scatter_elements_with_negative_indices() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, -3]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis)
+        # print(y) produces
+        # [[1.0, 1.1, 2.1, 4.0, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_with_negative_indices",
+        )
+
+    @staticmethod
+    def export_scatter_elements_with_duplicate_indices() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+            reduction="add",
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 1]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis, reduction="add")
+        # print(y) produces
+        # [[1.0, 5.2, 3.0, 4.0, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_with_duplicate_indices",
+        )
+
+    @staticmethod
+    def export_scatter_elements_with_reduction_max() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+            reduction="max",
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 1]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis, reduction="max")
+        # print(y) produces
+        # [[1.0, 2.1, 3.0, 4.0, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_with_reduction_max",
+        )
+
+    @staticmethod
+    def export_scatter_elements_with_reduction_min() -> None:
+        axis = 1
+        node = onnx.helper.make_node(
+            "ScatterElements",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            axis=axis,
+            reduction="min",
+        )
+        data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+        indices = np.array([[1, 1]], dtype=np.int64)
+        updates = np.array([[1.1, 2.1]], dtype=np.float32)
+
+        y = scatter_elements(data, indices, updates, axis, reduction="min")
+        # print(y) produces
+        # [[1.0, 1.1, 3.0, 4.0, 5.0]]
+
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[y],
+            name="test_scatter_elements_with_reduction_min",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatternd.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatternd.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6f7222877fe3bab6dc157786d1d1ba88cc54b2f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/scatternd.py
@@ -0,0 +1,223 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def scatter_nd_impl(data, indices, updates, reduction="none"):  # type: ignore
+    # Check tensor shapes
+    assert indices.shape[-1] <= len(data.shape)
+    assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1] :]
+
+    # Compute output
+    output = np.copy(data)
+    for i in np.ndindex(indices.shape[:-1]):
+        # NOTE: The order of iteration in this loop is not specified.
+        if reduction == "add":
+            output[tuple(indices[i])] += updates[i]
+        elif reduction == "mul":
+            output[tuple(indices[i])] *= updates[i]
+        elif reduction == "max":
+            output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
+        elif reduction == "min":
+            output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
+        else:
+            output[tuple(indices[i])] = updates[i]
+    return output
+
+
+class ScatterND(Base):
+    @staticmethod
+    def export_scatternd() -> None:
+        node = onnx.helper.make_node(
+            "ScatterND",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+        )
+        data = np.array(
+            [
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+            ],
+            dtype=np.float32,
+        )
+        indices = np.array([[0], [2]], dtype=np.int64)
+        updates = np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+            ],
+            dtype=np.float32,
+        )
+        # Expecting output as np.array(
+        #    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates)
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[output],
+            name="test_scatternd",
+        )
+
+    @staticmethod
+    def export_scatternd_add() -> None:
+        node = onnx.helper.make_node(
+            "ScatterND",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            reduction="add",
+        )
+        data = np.array(
+            [
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+            ],
+            dtype=np.float32,
+        )
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+            ],
+            dtype=np.float32,
+        )
+        # Expecting output as np.array(
+        #    [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction="add")
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[output],
+            name="test_scatternd_add",
+        )
+
+    @staticmethod
+    def export_scatternd_multiply() -> None:
+        node = onnx.helper.make_node(
+            "ScatterND",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            reduction="mul",
+        )
+        data = np.array(
+            [
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+            ],
+            dtype=np.float32,
+        )
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+            ],
+            dtype=np.float32,
+        )
+        # Expecting output as np.array(
+        #    [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction="mul")
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[output],
+            name="test_scatternd_multiply",
+        )
+
+    @staticmethod
+    def export_scatternd_max() -> None:
+        node = onnx.helper.make_node(
+            "ScatterND",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            reduction="max",
+        )
+        data = np.array(
+            [
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+            ],
+            dtype=np.float32,
+        )
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+            ],
+            dtype=np.float32,
+        )
+        # Expecting output as np.array(
+        #    [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction="max")
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[output],
+            name="test_scatternd_max",
+        )
+
+    @staticmethod
+    def export_scatternd_min() -> None:
+        node = onnx.helper.make_node(
+            "ScatterND",
+            inputs=["data", "indices", "updates"],
+            outputs=["y"],
+            reduction="min",
+        )
+        data = np.array(
+            [
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+                [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
+            ],
+            dtype=np.float32,
+        )
+        indices = np.array([[0], [0]], dtype=np.int64)
+        updates = np.array(
+            [
+                [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+                [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+            ],
+            dtype=np.float32,
+        )
+        # Expecting output as np.array(
+        #    [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]],
+        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
+        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
+        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
+        output = scatter_nd_impl(data, indices, updates, reduction="min")
+        expect(
+            node,
+            inputs=[data, indices, updates],
+            outputs=[output],
+            name="test_scatternd_min",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/selu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/selu.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fd523806ec0bb76959391e2210037df0923e247
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/selu.py
@@ -0,0 +1,49 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Selu(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Selu", inputs=["x"], outputs=["y"], alpha=2.0, gamma=3.0
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        # expected output [-3.79272318, 0., 3.]
+        y = (
+            np.clip(x, 0, np.inf) * 3.0
+            + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_selu_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = (
+            np.clip(x, 0, np.inf) * 3.0
+            + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 2.0 * 3.0
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_selu")
+
+    @staticmethod
+    def export_selu_default() -> None:
+        default_alpha = 1.67326319217681884765625
+        default_gamma = 1.05070102214813232421875
+        node = onnx.helper.make_node(
+            "Selu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = (
+            np.clip(x, 0, np.inf) * default_gamma
+            + (np.exp(np.clip(x, -np.inf, 0)) - 1) * default_alpha * default_gamma
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_selu_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequence_map.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequence_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ca9295bba9863237c64af52912a32cab495cf29
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequence_map.py
@@ -0,0 +1,304 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class SequenceMap(Base):
+    @staticmethod
+    def export_sequence_map_identity_1_sequence():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node("Identity", ["in0"], ["out0"])],
+            "seq_map_body",
+            [onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])],
+            [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["x"], outputs=["y"], body=body
+        )
+
+        x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)]
+        y = x
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_identity_1_sequence",
+        )
+
+    @staticmethod
+    def export_sequence_map_identity_2_sequences():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [
+                onnx.helper.make_node("Identity", ["in0"], ["out0"]),
+                onnx.helper.make_node("Identity", ["in1"], ["out1"]),
+            ],
+            "seq_map_body",
+            [
+                onnx.helper.make_tensor_value_info(
+                    "in0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "in1", onnx.TensorProto.FLOAT, ["M"]
+                ),
+            ],
+            [
+                onnx.helper.make_tensor_value_info(
+                    "out0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "out1", onnx.TensorProto.FLOAT, ["M"]
+                ),
+            ],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
+        )
+
+        x0 = [
+            np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
+            for _ in range(3)
+        ]
+        x1 = [
+            np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
+            for _ in range(3)
+        ]
+        y0 = x0
+        y1 = x1
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
+            ),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x0, x1],
+            outputs=[y0, y1],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_identity_2_sequences",
+        )
+
+    @staticmethod
+    def export_sequence_map_identity_1_sequence_1_tensor():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [
+                onnx.helper.make_node("Identity", ["in0"], ["out0"]),
+                onnx.helper.make_node("Identity", ["in1"], ["out1"]),
+            ],
+            "seq_map_body",
+            [
+                onnx.helper.make_tensor_value_info(
+                    "in0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "in1", onnx.TensorProto.FLOAT, ["M"]
+                ),
+            ],
+            [
+                onnx.helper.make_tensor_value_info(
+                    "out0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "out1", onnx.TensorProto.FLOAT, ["M"]
+                ),
+            ],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
+        )
+
+        x0 = [
+            np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
+            for _ in range(3)
+        ]
+        x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
+        y0 = x0
+        y1 = [x1 for _ in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x0, x1],
+            outputs=[y0, y1],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_identity_1_sequence_1_tensor",
+        )
+
+    @staticmethod
+    def export_sequence_map_add_2_sequences():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
+            "seq_map_body",
+            [
+                onnx.helper.make_tensor_value_info(
+                    "in0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "in1", onnx.TensorProto.FLOAT, ["N"]
+                ),
+            ],
+            [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
+        )
+
+        N = [np.random.randint(1, 10) for _ in range(3)]
+        x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
+        x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
+        y0 = [x0[k] + x1[k] for k in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x0, x1],
+            outputs=[y0],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_add_2_sequences",
+        )
+
+    @staticmethod
+    def export_sequence_map_add_1_sequence_1_tensor():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
+            "seq_map_body",
+            [
+                onnx.helper.make_tensor_value_info(
+                    "in0", onnx.TensorProto.FLOAT, ["N"]
+                ),
+                onnx.helper.make_tensor_value_info(
+                    "in1", onnx.TensorProto.FLOAT, ["N"]
+                ),
+            ],
+            [onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
+        )
+
+        x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
+        x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
+        y0 = [x0[i] + x1 for i in range(3)]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+            onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x0, x1],
+            outputs=[y0],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_add_1_sequence_1_tensor",
+        )
+
+    @staticmethod
+    def export_sequence_map_extract_shapes():  # type: () -> None
+        body = onnx.helper.make_graph(
+            [onnx.helper.make_node("Shape", ["x"], ["shape"])],
+            "seq_map_body",
+            [
+                onnx.helper.make_tensor_value_info(
+                    "x", onnx.TensorProto.FLOAT, ["H", "W", "C"]
+                )
+            ],
+            [onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])],
+        )
+
+        node = onnx.helper.make_node(
+            "SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body
+        )
+
+        shapes = [
+            np.array([40, 30, 3], dtype=np.int64),
+            np.array([20, 10, 3], dtype=np.int64),
+            np.array([10, 5, 3], dtype=np.int64),
+        ]
+        x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
+        input_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(
+                    onnx.TensorProto.FLOAT, ["H", "W", "C"]
+                )
+            ),
+        ]
+        output_type_protos = [
+            onnx.helper.make_sequence_type_proto(
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])
+            ),
+        ]
+        expect(
+            node,
+            inputs=[x0],
+            outputs=[shapes],
+            input_type_protos=input_type_protos,
+            output_type_protos=output_type_protos,
+            name="test_sequence_map_extract_shapes",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequenceinsert.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequenceinsert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac50aab17d89125c91288c44b6543cd9388137be
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sequenceinsert.py
@@ -0,0 +1,75 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def sequence_insert_reference_implementation(
+    sequence: list[Any], tensor: np.ndarray, position: np.ndarray = None
+) -> list[Any]:
+    # make a copy of input sequence
+    seq = list(sequence)
+    if position is not None:
+        # In these cases, insert_position will be between [-len(sequence), len(sequence)]
+        # The position argument will be in the format np.array([pos_index])
+        insert_position = position[0]
+        seq.insert(insert_position, tensor)
+    else:
+        # Default position of insertion is at the end of the sequence.
+        seq.append(tensor)
+    return seq
+
+
+class SequenceInsert(Base):
+    @staticmethod
+    def export() -> None:
+        test_cases = {
+            "at_back": [np.array([10, 11, 12]).astype(np.int64)],
+            "at_front": [np.array([-2, -1, 0]), np.array([0]).astype(np.int64)],
+        }
+        sequence = [
+            np.array([1, 2, 3, 4]).astype(np.int64),
+            np.array([5, 6, 7]).astype(np.int64),
+            np.array([8, 9]).astype(np.int64),
+        ]
+
+        for test_name, test_inputs in test_cases.items():
+            tensor = test_inputs[0].astype(np.int64)
+
+            if len(test_inputs) > 1:
+                node = onnx.helper.make_node(
+                    "SequenceInsert",
+                    inputs=["sequence", "tensor", "position"],
+                    outputs=["output_sequence"],
+                )
+                position = test_inputs[1]
+                inserted = sequence_insert_reference_implementation(
+                    sequence, tensor, position
+                )
+                expect(
+                    node,
+                    inputs=[sequence, tensor, position],
+                    outputs=[inserted],
+                    name="test_sequence_insert_" + test_name,
+                )
+            else:
+                node = onnx.helper.make_node(
+                    "SequenceInsert",
+                    inputs=["sequence", "tensor"],
+                    outputs=["output_sequence"],
+                )
+                inserted = sequence_insert_reference_implementation(sequence, tensor)
+                expect(
+                    node,
+                    inputs=[sequence, tensor],
+                    outputs=[inserted],
+                    name="test_sequence_insert_" + test_name,
+                )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shape.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shape.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bd79e28f1617be254faeb31d0c1155b76173a8f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shape.py
@@ -0,0 +1,60 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+# Reference implementation of shape op
+def shape_reference_impl(x, start=None, end=None):  # type: ignore
+    dims = x.shape[start:end]
+    return np.array(dims).astype(np.int64)
+
+
+def test_shape(testname, xval, start=None, end=None):  # type: ignore
+    node = onnx.helper.make_node(
+        "Shape", inputs=["x"], outputs=["y"], start=start, end=end
+    )
+
+    yval = shape_reference_impl(xval, start, end)
+
+    expect(node, inputs=[xval], outputs=[yval], name="test_shape" + testname)
+
+
+class Shape(Base):
+    @staticmethod
+    def export() -> None:
+        x = np.array(
+            [
+                [1, 2, 3],
+                [4, 5, 6],
+            ]
+        ).astype(np.float32)
+        test_shape("_example", x)  # preserve names of original test cases
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+
+        test_shape("", x)  # preserve names of original test cases
+
+        test_shape("_start_1", x, start=1)
+
+        test_shape("_end_1", x, end=1)
+
+        test_shape("_start_negative_1", x, start=-1)
+
+        test_shape("_end_negative_1", x, end=-1)
+
+        test_shape("_start_1_end_negative_1", x, start=1, end=-1)
+
+        test_shape("_start_1_end_2", x, start=1, end=2)
+
+        test_shape("_clip_start", x, start=-10)
+
+        test_shape("_clip_end", x, end=10)
+
+        test_shape("_start_greater_than_end", x, start=2, end=1)
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shrink.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shrink.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d7627e16f84db996a35a052f97790ba2f4e3025
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/shrink.py
@@ -0,0 +1,37 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Shrink(Base):
+    @staticmethod
+    def export_hard_shrink() -> None:
+        node = onnx.helper.make_node(
+            "Shrink",
+            inputs=["x"],
+            outputs=["y"],
+            lambd=1.5,
+        )
+        X = np.arange(-2.0, 2.1, dtype=np.float32)
+        Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
+        expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard")
+
+    @staticmethod
+    def export_soft_shrink() -> None:
+        node = onnx.helper.make_node(
+            "Shrink",
+            inputs=["x"],
+            outputs=["y"],
+            lambd=1.5,
+            bias=1.5,
+        )
+        X = np.arange(-2.0, 2.1, dtype=np.float32)
+        Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
+        expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sigmoid.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sigmoid.py
new file mode 100644
index 0000000000000000000000000000000000000000..1010b3ee1062a9097a1a5adb9a7177570459d4a4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sigmoid.py
@@ -0,0 +1,30 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Sigmoid(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Sigmoid",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = 1.0 / (
+            1.0 + np.exp(np.negative(x))
+        )  # expected output [0.26894143, 0.5, 0.7310586]
+        expect(node, inputs=[x], outputs=[y], name="test_sigmoid_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = 1.0 / (1.0 + np.exp(np.negative(x)))
+        expect(node, inputs=[x], outputs=[y], name="test_sigmoid")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sign.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sign.py
new file mode 100644
index 0000000000000000000000000000000000000000..8be10f4ae6de2e38739bf521c6c3123c7cdbb4d5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sign.py
@@ -0,0 +1,24 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Sign(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Sign",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array(range(-5, 6)).astype(np.float32)
+        y = np.sign(x)
+        expect(node, inputs=[x], outputs=[y], name="test_sign")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sin.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sin.py
new file mode 100644
index 0000000000000000000000000000000000000000..5aed7fbd85d2fd32a0be4eb39ac73974313affe8
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sin.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Sin(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Sin",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.sin(x)
+        expect(node, inputs=[x], outputs=[y], name="test_sin_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.sin(x)
+        expect(node, inputs=[x], outputs=[y], name="test_sin")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sinh.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sinh.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b4b6738f471e3d7f9a19e7131fa8e9452580ee
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sinh.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Sinh(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Sinh",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.sinh(x)  # expected output [-1.17520118,  0.,  1.17520118]
+        expect(node, inputs=[x], outputs=[y], name="test_sinh_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.sinh(x)
+        expect(node, inputs=[x], outputs=[y], name="test_sinh")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/size.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/size.py
new file mode 100644
index 0000000000000000000000000000000000000000..f88387b4535f32e31e296f2f3937d6bbe864a63a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/size.py
@@ -0,0 +1,35 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Size(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Size",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array(
+            [
+                [1, 2, 3],
+                [4, 5, 6],
+            ]
+        ).astype(np.float32)
+        y = np.array(6).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y], name="test_size_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.array(x.size).astype(np.int64)
+
+        expect(node, inputs=[x], outputs=[y], name="test_size")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/slice.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/slice.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a11e99229c2aba626662dfea5b52c6c5e9c59af
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/slice.py
@@ -0,0 +1,178 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Slice(Base):
+    @staticmethod
+    def export_slice() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes", "steps"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        y = x[0:3, 0:10]
+        starts = np.array([0, 0], dtype=np.int64)
+        ends = np.array([3, 10], dtype=np.int64)
+        axes = np.array([0, 1], dtype=np.int64)
+        steps = np.array([1, 1], dtype=np.int64)
+
+        expect(
+            node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice"
+        )
+
+    @staticmethod
+    def export_slice_neg() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes", "steps"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0], dtype=np.int64)
+        ends = np.array([-1], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 0:-1]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes, steps],
+            outputs=[y],
+            name="test_slice_neg",
+        )
+
+    @staticmethod
+    def export_slice_start_out_of_bounds() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes", "steps"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([1000], dtype=np.int64)
+        ends = np.array([1000], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 1000:1000]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes, steps],
+            outputs=[y],
+            name="test_slice_start_out_of_bounds",
+        )
+
+    @staticmethod
+    def export_slice_end_out_of_bounds() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes", "steps"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([1], dtype=np.int64)
+        ends = np.array([1000], dtype=np.int64)
+        axes = np.array([1], dtype=np.int64)
+        steps = np.array([1], dtype=np.int64)
+        y = x[:, 1:1000]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes, steps],
+            outputs=[y],
+            name="test_slice_end_out_of_bounds",
+        )
+
+    @staticmethod
+    def export_slice_default_axes() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(
+            node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes"
+        )
+
+    @staticmethod
+    def export_slice_default_steps() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        axes = np.array([0, 1, 2], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes],
+            outputs=[y],
+            name="test_slice_default_steps",
+        )
+
+    @staticmethod
+    def export_slice_neg_steps() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes", "steps"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([20, 10, 4], dtype=np.int64)
+        ends = np.array([0, 0, 1], dtype=np.int64)
+        axes = np.array([0, 1, 2], dtype=np.int64)
+        steps = np.array([-1, -3, -2]).astype(np.int64)
+        y = x[20:0:-1, 10:0:-3, 4:1:-2]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes, steps],
+            outputs=[y],
+            name="test_slice_neg_steps",
+        )
+
+    @staticmethod
+    def export_slice_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            "Slice",
+            inputs=["x", "starts", "ends", "axes"],
+            outputs=["y"],
+        )
+
+        x = np.random.randn(20, 10, 5).astype(np.float32)
+        starts = np.array([0, 0, 3], dtype=np.int64)
+        ends = np.array([20, 10, 4], dtype=np.int64)
+        axes = np.array([0, -2, -1], dtype=np.int64)
+        y = x[:, :, 3:4]
+
+        expect(
+            node,
+            inputs=[x, starts, ends, axes],
+            outputs=[y],
+            name="test_slice_negative_axes",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmax.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..e01efba8e3029326aff7e133a8e4839f769ae847
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmax.py
@@ -0,0 +1,91 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_max = np.max(x, axis=axis, keepdims=True)
+    tmp = np.exp(x - x_max)
+    s = np.sum(tmp, axis=axis, keepdims=True)
+    return tmp / s
+
+
+class Softmax(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        x = np.array([[-1, 0, 1]]).astype(np.float32)
+        # expected output [[0.09003058, 0.24472848, 0.66524094]]
+        y = softmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_example")
+
+    @staticmethod
+    def export_softmax_axis() -> None:
+        x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
+        # expected output
+        # [[0.032058604 0.08714432  0.23688284  0.6439143  ]
+        # [0.032058604 0.08714432  0.23688284  0.6439143  ]]
+        y = softmax(x)
+
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_large_number")
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=0,
+        )
+        y = softmax(x, axis=0)
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_0")
+
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=1,
+        )
+        y = softmax(x, axis=1)
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_1")
+
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=2,
+        )
+        y = softmax(x, axis=2)
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_axis_2")
+
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+            axis=-1,
+        )
+        y = softmax(x, axis=-1)
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_negative_axis")
+
+        # default axis is -1
+        node = onnx.helper.make_node(
+            "Softmax",
+            inputs=["x"],
+            outputs=["y"],
+        )
+        expect(node, inputs=[x], outputs=[y], name="test_softmax_default_axis")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmaxcrossentropy.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmaxcrossentropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeeaded119fbcf912f6e3c052ebc8fb212f2b105
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softmaxcrossentropy.py
@@ -0,0 +1,1152 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def softmaxcrossentropy(
+    x, target, weight=None, reduction="mean", ignore_index=None, get_log_prob=None
+):  # type: ignore
+    input_shape = x.shape
+    if len(input_shape) == 1:
+        raise RuntimeError("Unsupported shape")
+
+    target_shape = target.shape
+    N = input_shape[0]
+    C = input_shape[1]
+
+    # compute log_softmax
+    max_x = np.max(x, axis=1, keepdims=True)
+    exp_x = np.exp(x - max_x)
+    p = exp_x / np.sum(exp_x, axis=1, keepdims=True)
+    inp = np.log(p)
+    log_prob = None
+    if get_log_prob is True:
+        log_prob = np.copy(inp)
+
+    # initialize the positional weights when required
+    gather_weight = None
+    if weight is not None:
+        # setting mode='clip' to deal with ignore_index > C or < 0 cases.
+        # when the target value is > C or < 0, it doesn't matter which value we are
+        # taking in gather_weight, since it will be set to 0 in the following if-block
+        # use np.int32 to make it compatible with x86 machines
+        gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
+        # set `ignore_index`'s loss weight to 0.
+        # The loss tensor will be multiplied by this weight tensor,
+        # so `ingore_index`'s loss value will be eliminated.
+        if ignore_index is not None:
+            gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
+                dtype=np.float32
+            )
+    elif ignore_index is not None:
+        gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=np.float32)
+
+    # if input is 4-d and above, make it 3-d
+    if len(input_shape) != 3:
+        inp = inp.reshape((N, C, -1))
+        target = target.reshape((N, -1))
+
+    # Get a dimension from the reshaped input.
+    # If the original input shape is [N, C, H, W],
+    # the D here should be H * W because we reshape
+    # [N, C, H, W] to [N, C, H * W].
+    D = inp.shape[2]
+    neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
+    for i in range(N):
+        for d in range(D):
+            if target[i][d] != ignore_index:
+                neg_gather_element_input[i][d] = -inp[i][target[i][d]][d]
+
+    loss = neg_gather_element_input
+
+    # if the input was 4-d or above reshape to the right shape
+    if len(input_shape) != 3:
+        loss = loss.reshape(target_shape)
+
+    # apply the weights when required
+    if gather_weight is not None:
+        loss = gather_weight * loss
+        if reduction == "mean":
+            loss = loss.sum() / gather_weight.sum()
+            if get_log_prob is True:
+                return loss, log_prob
+            else:
+                return loss
+
+    if reduction == "mean":
+        loss = np.mean(loss)
+    elif reduction == "sum":
+        loss = np.sum(loss)
+
+    if get_log_prob:
+        return loss, log_prob
+    return loss
+
+
+class SoftmaxCrossEntropyLoss(Base):
+    @staticmethod
+    def export_softmaxcrossentropy_none() -> None:
+        # Define operator attributes.
+        reduction = "none"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction="none")
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_none")
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "none"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, reduction="none", get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_none_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_weights() -> None:
+        # Define operator attributes.
+        reduction = "none"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, reduction="none")
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[sce],
+            name="test_sce_none_weights",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_none_weights_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "none"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, weight=weights, reduction="none", get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[loss, log_prob],
+            name="test_sce_none_weights_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_sum() -> None:
+        # Define operator attributes.
+        reduction = "sum"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, reduction="sum")
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_sum")
+
+    @staticmethod
+    def export_softmaxcrossentropy_sum_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "sum"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, reduction="sum", get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_sum_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels)
+
+        # Check results
+        expect(node, inputs=[x, labels], outputs=[sce], name="test_sce_mean")
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, labels, get_log_prob=True)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_3d() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, y)
+
+        # Check results
+        expect(node, inputs=[x, y], outputs=[sce], name="test_sce_mean_3d")
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        y = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(x, y, get_log_prob=True)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, y],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_3d_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[sce],
+            name="test_sce_mean_weight",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, weight=weights, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_weight_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(0)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        labels[0] = np.int64(0)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[sce],
+            name="test_sce_mean_weight_ii",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(0)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        labels[0] = np.int64(0)
+        weights = np.array([0.9, 0.7, 0.8, 0.9, 0.9], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_weight_ii_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        labels[0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+        # Check results
+        expect(
+            node, inputs=[x, labels], outputs=[sce], name="test_sce_mean_no_weight_ii"
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3,)).astype(np.int64)
+        labels[0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_no_weight_ii_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_3d() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(1)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(1)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, weight=weights, ignore_index=ignore_index)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[sce],
+            name="test_sce_mean_weight_ii_3d",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(1)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(1)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, weight=weights, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_weight_ii_3d_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_3d() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(x, labels, ignore_index=ignore_index)
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[sce],
+            name="test_sce_mean_no_weight_ii_3d",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_3d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2)).astype(np.int64)
+        labels[0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_no_weight_ii_3d_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_4d() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(
+            x, labels, reduction=reduction, weight=weights, ignore_index=ignore_index
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[sce],
+            name="test_sce_mean_weight_ii_4d",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_weights_ii_4d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+        weights = np.array([0.2, 0.3, 0.6, 0.1, 0.5], dtype=np.float32)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x,
+            labels,
+            reduction=reduction,
+            weight=weights,
+            ignore_index=ignore_index,
+            get_log_prob=True,
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels, weights],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_weight_ii_4d_log_prob",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_4d() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        sce = softmaxcrossentropy(
+            x, labels, reduction=reduction, ignore_index=ignore_index
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[sce],
+            name="test_sce_mean_no_weight_ii_4d",
+        )
+
+    @staticmethod
+    def export_softmaxcrossentropy_mean_no_weights_ii_4d_log_prob() -> None:
+        # Define operator attributes.
+        reduction = "mean"
+        ignore_index = np.int64(2)
+
+        # Create operator.
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        # Define operator inputs.
+        np.random.seed(0)
+        x = np.random.rand(3, 5, 2, 7).astype(np.float32)
+        labels = np.random.randint(0, high=5, size=(3, 2, 7)).astype(np.int64)
+        labels[0][0][0] = np.int64(2)
+
+        # Compute SoftmaxCrossEntropyLoss
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        # Check results
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_mean_no_weight_ii_4d_log_prob",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight() -> None:
+        reduction = "mean"
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(x, labels, weight=weight, reduction=reduction)
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[sce],
+            name="test_sce_NCd1d2d3d4d5_mean_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_mean_weight_log_prob() -> None:
+        reduction = "mean"
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, weight=weight, reduction=reduction, get_log_prob=True
+        )
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[loss, log_prob],
+            name="test_sce_NCd1d2d3d4d5_mean_weight_log_prob",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight() -> None:
+        reduction = "none"
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+
+        sce = softmaxcrossentropy(x, labels, reduction=reduction)
+
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[sce],
+            name="test_sce_NCd1d2d3d4d5_none_no_weight",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3d4d5_none_no_weight_log_prob() -> None:
+        reduction = "none"
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+        )
+
+        N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
+        labels = np.random.randint(
+            0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
+        ).astype(np.int64)
+
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, reduction=reduction, get_log_prob=True
+        )
+
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_NCd1d2d3d4d5_none_no_weight_log_prob",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        labels[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(
+            x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[sce],
+            name="test_sce_NCd1_mean_weight_negative_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1_mean_weight_negative_ii_log_prob() -> None:
+        reduction = "mean"
+        ignore_index = np.int64(-1)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1 = 3, 5, 6
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
+        labels[0][0] = -1
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(
+            x,
+            labels,
+            weight=weight,
+            reduction=reduction,
+            ignore_index=ignore_index,
+            get_log_prob=True,
+        )
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[loss, log_prob],
+            name="test_sce_NCd1_mean_weight_negative_ii_log_prob",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii() -> None:
+        reduction = "none"
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
+            np.int64
+        )
+        labels[0][0][0][0] = -5
+
+        sce = softmaxcrossentropy(
+            x, labels, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[sce],
+            name="test_sce_NCd1d2d3_none_no_weight_negative_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_none_no_weight_negative_ii_log_prob() -> None:
+        reduction = "none"
+        ignore_index = np.int64(-5)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
+            np.int64
+        )
+        labels[0][0][0][0] = -5
+
+        loss, log_prob = softmaxcrossentropy(
+            x, labels, reduction=reduction, ignore_index=ignore_index, get_log_prob=True
+        )
+
+        expect(
+            node,
+            inputs=[x, labels],
+            outputs=[loss, log_prob],
+            name="test_sce_NCd1d2d3_none_no_weight_negative_ii_log_prob",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii() -> None:
+        reduction = "sum"
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C = 3, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+        labels[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        sce = softmaxcrossentropy(
+            x, labels, weight=weight, reduction=reduction, ignore_index=ignore_index
+        )
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[sce],
+            name="test_sce_NCd1d2d3_sum_weight_high_ii",
+        )
+
+    @staticmethod
+    def export_input_shape_is_NCd1d2d3_sum_weight_high_ii_log_prob() -> None:
+        reduction = "sum"
+        ignore_index = np.int64(10)
+
+        node = onnx.helper.make_node(
+            "SoftmaxCrossEntropyLoss",
+            inputs=["x", "y", "w"],
+            outputs=["z", "log_prob"],
+            reduction=reduction,
+            ignore_index=ignore_index,
+        )
+
+        N, C = 3, 5
+        np.random.seed(0)
+        x = np.random.rand(N, C).astype(np.float32)
+        labels = np.random.randint(0, high=C, size=(N)).astype(np.int64)
+        labels[0] = 10
+        weight = np.random.rand(C).astype(np.float32)
+
+        loss, log_prob = softmaxcrossentropy(
+            x,
+            labels,
+            weight=weight,
+            reduction=reduction,
+            ignore_index=ignore_index,
+            get_log_prob=True,
+        )
+
+        expect(
+            node,
+            inputs=[x, labels, weight],
+            outputs=[loss, log_prob],
+            name="test_sce_NCd1d2d3_sum_weight_high_ii_log_prob",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softplus.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softplus.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4581c0f2a4520bc7988604ae88aab6ed5c3403e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softplus.py
@@ -0,0 +1,30 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Softplus(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Softplus",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.log(
+            np.exp(x) + 1
+        )  # expected output [0.31326166, 0.69314718, 1.31326163]
+        expect(node, inputs=[x], outputs=[y], name="test_softplus_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.log(np.exp(x) + 1)
+        expect(node, inputs=[x], outputs=[y], name="test_softplus")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softsign.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softsign.py
new file mode 100644
index 0000000000000000000000000000000000000000..759ceb6f039c41f6f6811082807ef1c463255f45
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/softsign.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Softsign(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Softsign",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.array([-0.5, 0, 0.5]).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_softsign_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = x / (1 + np.abs(x))
+        expect(node, inputs=[x], outputs=[y], name="test_softsign")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/spacetodepth.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/spacetodepth.py
new file mode 100644
index 0000000000000000000000000000000000000000..f74a6e5a8c04dca06828a4a8ab1cd399181e9ff2
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/spacetodepth.py
@@ -0,0 +1,66 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class SpaceToDepth(Base):
+    @staticmethod
+    def export() -> None:
+        b, c, h, w = shape = (2, 2, 6, 6)
+        blocksize = 2
+        node = onnx.helper.make_node(
+            "SpaceToDepth",
+            inputs=["x"],
+            outputs=["y"],
+            blocksize=blocksize,
+        )
+        x = np.random.random_sample(shape).astype(np.float32)
+        tmp = np.reshape(
+            x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]
+        )
+        tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
+        y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
+        expect(node, inputs=[x], outputs=[y], name="test_spacetodepth")
+
+    @staticmethod
+    def export_example() -> None:
+        node = onnx.helper.make_node(
+            "SpaceToDepth",
+            inputs=["x"],
+            outputs=["y"],
+            blocksize=2,
+        )
+
+        # (1, 1, 4, 6) input tensor
+        x = np.array(
+            [
+                [
+                    [
+                        [0, 6, 1, 7, 2, 8],
+                        [12, 18, 13, 19, 14, 20],
+                        [3, 9, 4, 10, 5, 11],
+                        [15, 21, 16, 22, 17, 23],
+                    ]
+                ]
+            ]
+        ).astype(np.float32)
+
+        # (1, 4, 2, 3) output tensor
+        y = np.array(
+            [
+                [
+                    [[0, 1, 2], [3, 4, 5]],
+                    [[6, 7, 8], [9, 10, 11]],
+                    [[12, 13, 14], [15, 16, 17]],
+                    [[18, 19, 20], [21, 22, 23]],
+                ]
+            ]
+        ).astype(np.float32)
+        expect(node, inputs=[x], outputs=[y], name="test_spacetodepth_example")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/split.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/split.py
new file mode 100644
index 0000000000000000000000000000000000000000..99587806c70827d3ab04fbc14baf4cd77963246a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/split.py
@@ -0,0 +1,378 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Split(Base):
+    @staticmethod
+    def export_1d_opset13() -> None:
+        node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2", "output_3"],
+            axis=0,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0]).astype(np.float32),
+            np.array([5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_1d_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2"],
+            axis=0,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_1d_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+    @staticmethod
+    def export_2d_opset13() -> None:
+        node_input = np.array(
+            [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
+        ).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1
+        )
+
+        expected_outputs = [
+            np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
+            np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
+        ]
+
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_2d_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2"],
+            axis=1,
+        )
+
+        expected_outputs = [
+            np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
+            np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
+                np.float32
+            ),
+        ]
+
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_2d_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+    @staticmethod
+    def export_default_values_opset13() -> None:
+        node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
+
+        # If axis is not specified, split is applied on default axis 0
+        node = onnx.helper.make_node(
+            "Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"]
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0]).astype(np.float32),
+            np.array([5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_default_axis_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_default_axis_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+    @staticmethod
+    def export_zero_size_splits_opset13() -> None:
+        # 1-dimensional tensor with dimension_size=0
+        node_input = np.array([]).astype(np.float32)
+
+        # Split empty tensor to tensors of size zero
+        split = np.array([0, 0, 0]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2", "output_3"],
+        )
+
+        expected_outputs = [
+            np.array([]).astype(np.float32),
+            np.array([]).astype(np.float32),
+            np.array([]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_zero_size_splits_opset13",
+            opset_imports=[onnx.helper.make_opsetid("", 13)],
+        )
+
+    @staticmethod
+    def export_1d_opset18() -> None:
+        node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2", "output_3"],
+            axis=0,
+            num_outputs=3,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0]).astype(np.float32),
+            np.array([5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_1d_opset18",
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2"],
+            axis=0,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_1d_opset18",
+        )
+
+    @staticmethod
+    def export_2d_opset18() -> None:
+        node_input = np.array(
+            [[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
+        ).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2"],
+            axis=1,
+            num_outputs=2,
+        )
+
+        expected_outputs = [
+            np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
+            np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
+        ]
+
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_2d",
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2"],
+            axis=1,
+        )
+
+        expected_outputs = [
+            np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
+            np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
+                np.float32
+            ),
+        ]
+
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_2d_opset18",
+        )
+
+    @staticmethod
+    def export_default_values_opset18() -> None:
+        node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
+
+        # If axis is not specified, split is applied on default axis 0
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2", "output_3"],
+            num_outputs=3,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0]).astype(np.float32),
+            np.array([5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_equal_parts_default_axis_opset18",
+        )
+
+        split = np.array([2, 4]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_variable_parts_default_axis_opset18",
+        )
+
+    @staticmethod
+    def export_zero_size_splits_opset18() -> None:
+        # 1-dimensional tensor with dimension_size=0
+        node_input = np.array([]).astype(np.float32)
+
+        # Split empty tensor to tensors of size zero
+        split = np.array([0, 0, 0]).astype(np.int64)
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input", "split"],
+            outputs=["output_1", "output_2", "output_3"],
+        )
+
+        expected_outputs = [
+            np.array([]).astype(np.float32),
+            np.array([]).astype(np.float32),
+            np.array([]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input, split],
+            outputs=expected_outputs,
+            name="test_split_zero_size_splits_opset18",
+        )
+
+    @staticmethod
+    def export_1d_uneven_split_opset18() -> None:
+        node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32)
+
+        # If axis is not specified, split is applied on default axis 0
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2", "output_3", "output_4"],
+            num_outputs=4,
+        )
+
+        expected_outputs = [
+            np.array([1.0, 2.0]).astype(np.float32),
+            np.array([3.0, 4.0]).astype(np.float32),
+            np.array([5.0, 6.0]).astype(np.float32),
+            np.array([7.0]).astype(np.float32),
+        ]
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_1d_uneven_split_opset18",
+        )
+
+    @staticmethod
+    def export_2d_uneven_split_opset18() -> None:
+        node_input = np.array(
+            [
+                [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
+                [9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
+            ]
+        ).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Split",
+            inputs=["input"],
+            outputs=["output_1", "output_2", "output_3"],
+            axis=1,
+            num_outputs=3,
+        )
+
+        expected_outputs = [
+            np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32),
+            np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32),
+            np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32),
+        ]
+
+        expect(
+            node,
+            inputs=[node_input],
+            outputs=expected_outputs,
+            name="test_split_2d_uneven_split_opset18",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/splittosequence.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/splittosequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..f37fd222a2f6296bcbae3f1434b8c00082b7a641
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/splittosequence.py
@@ -0,0 +1,80 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class SplitToSequence(Base):
+    @staticmethod
+    def export_with_split_1() -> None:
+        data = np.arange(18).reshape((3, 6)).astype(np.float32)
+        split = np.array(2, dtype=np.int64)
+
+        node = onnx.helper.make_node(
+            "SplitToSequence", ["data", "split"], ["seq"], axis=1
+        )
+
+        expected_outputs = [
+            [
+                np.array([[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]], dtype=np.float32),
+                np.array([[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]], dtype=np.float32),
+                np.array([[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]], dtype=np.float32),
+            ]
+        ]
+
+        expect(
+            node,
+            inputs=[data, split],
+            outputs=expected_outputs,
+            name="test_split_to_sequence_1",
+        )
+
+    @staticmethod
+    def export_with_split_2() -> None:
+        data = np.arange(18).reshape((3, 6)).astype(np.float32)
+        split = np.array([1, 2], dtype=np.int64)
+
+        node = onnx.helper.make_node(
+            "SplitToSequence", ["data", "split"], ["seq"], axis=0
+        )
+
+        expected_outputs = [
+            [
+                data[:1],
+                data[1:],
+            ]
+        ]
+
+        expect(
+            node,
+            inputs=[data, split],
+            outputs=expected_outputs,
+            name="test_split_to_sequence_2",
+        )
+
+    @staticmethod
+    def export_nokeepdims() -> None:
+        data = np.arange(18).reshape((3, 6)).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "SplitToSequence",
+            ["data"],
+            ["seq"],
+            axis=1,
+            keepdims=0,
+        )
+
+        expected_outputs = [[data[:, i] for i in range(data.shape[1])]]
+
+        expect(
+            node,
+            inputs=[data],
+            outputs=expected_outputs,
+            name="test_split_to_sequence_nokeepdims",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sqrt.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sqrt.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff82419fd8d9c047a7d3b991c5a7e0dc53071289
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/sqrt.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Sqrt(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Sqrt",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([1, 4, 9]).astype(np.float32)
+        y = np.sqrt(x)  # expected output [1., 2., 3.]
+        expect(node, inputs=[x], outputs=[y], name="test_sqrt_example")
+
+        x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
+        y = np.sqrt(x)
+        expect(node, inputs=[x], outputs=[y], name="test_sqrt")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/squeeze.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/squeeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f67e6647126bbd16c8ef5c3dc2ff7f6767bb63a
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/squeeze.py
@@ -0,0 +1,37 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Squeeze(Base):
+    @staticmethod
+    def export_squeeze() -> None:
+        node = onnx.helper.make_node(
+            "Squeeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        x = np.random.randn(1, 3, 4, 5).astype(np.float32)
+        axes = np.array([0], dtype=np.int64)
+        y = np.squeeze(x, axis=0)
+
+        expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze")
+
+    @staticmethod
+    def export_squeeze_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            "Squeeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+        axes = np.array([-2], dtype=np.int64)
+        y = np.squeeze(x, axis=-2)
+        expect(node, inputs=[x, axes], outputs=[y], name="test_squeeze_negative_axes")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stft.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stft.py
new file mode 100644
index 0000000000000000000000000000000000000000..71ed61b3c6696a54cf422b7d8f55c0e506fff01b
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stft.py
@@ -0,0 +1,70 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class STFT(Base):
+    @staticmethod
+    def export() -> None:
+        signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1)
+        length = np.array(16).astype(np.int64)
+        onesided_length = (length >> 1) + 1
+        step = np.array(8).astype(np.int64)
+
+        no_window = ""  # optional input, not supplied
+        node = onnx.helper.make_node(
+            "STFT",
+            inputs=["signal", "frame_step", no_window, "frame_length"],
+            outputs=["output"],
+        )
+
+        nstfts = ((signal.shape[1] - length) // step) + 1
+        # [batch_size][frames][frame_length][2]
+        output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
+        for i in range(nstfts):
+            start = i * step
+            stop = i * step + length
+            complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length]
+            output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
+
+        output = output.astype(signal.dtype)
+        expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft")
+
+        node = onnx.helper.make_node(
+            "STFT",
+            inputs=["signal", "frame_step", "window"],
+            outputs=["output"],
+        )
+
+        # Test with window
+        a0 = 0.5
+        a1 = 0.5
+        window = a0 + a1 * np.cos(
+            2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length
+        )
+        nstfts = 1 + (signal.shape[1] - window.shape[0]) // step
+
+        # [batch_size][frames][frame_length][2]
+        output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
+        for i in range(nstfts):
+            start = i * step
+            stop = i * step + length
+            complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[
+                0:onesided_length
+            ]
+            output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
+        window = window.astype(signal.dtype)
+        output = output.astype(signal.dtype)
+        expect(
+            node,
+            inputs=[signal, step, window],
+            outputs=[output],
+            name="test_stft_with_window",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_concat.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b5cfb17fdac3912eb227df037b8437b8ec612e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_concat.py
@@ -0,0 +1,69 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class StringConcat(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "StringConcat",
+            inputs=["x", "y"],
+            outputs=["result"],
+        )
+        x = np.array(["abc", "def"]).astype("object")
+        y = np.array([".com", ".net"]).astype("object")
+        result = np.array(["abc.com", "def.net"]).astype("object")
+
+        expect(node, inputs=[x, y], outputs=[result], name="test_string_concat")
+
+        x = np.array(["cat", "dog", "snake"]).astype("object")
+        y = np.array(["s"]).astype("object")
+        result = np.array(["cats", "dogs", "snakes"]).astype("object")
+
+        expect(
+            node,
+            inputs=[x, y],
+            outputs=[result],
+            name="test_string_concat_broadcasting",
+        )
+
+        x = np.array("cat").astype("object")
+        y = np.array("s").astype("object")
+        result = np.array("cats").astype("object")
+
+        expect(
+            node,
+            inputs=[x, y],
+            outputs=[result],
+            name="test_string_concat_zero_dimensional",
+        )
+
+        x = np.array(["abc", ""]).astype("object")
+        y = np.array(["", "abc"]).astype("object")
+        result = np.array(["abc", "abc"]).astype("object")
+
+        expect(
+            node,
+            inputs=[x, y],
+            outputs=[result],
+            name="test_string_concat_empty_string",
+        )
+
+        x = np.array(["的", "中"]).astype("object")
+        y = np.array(["的", "中"]).astype("object")
+        result = np.array(["的的", "中中"]).astype("object")
+
+        expect(
+            node,
+            inputs=[x, y],
+            outputs=[result],
+            name="test_string_concat_utf8",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_split.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..305bd578c8fe15bf5b50f5967998adaea5a6f6a5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/string_split.py
@@ -0,0 +1,151 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class StringSplit(Base):
+    @staticmethod
+    def export_basic() -> None:
+        node = onnx.helper.make_node(
+            "StringSplit",
+            inputs=["x"],
+            outputs=["substrings", "length"],
+            delimiter=".",
+            maxsplit=None,
+        )
+
+        x = np.array(["abc.com", "def.net"]).astype(object)
+
+        substrings = np.array([["abc", "com"], ["def", "net"]]).astype(object)
+
+        length = np.array([2, 2], dtype=np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[substrings, length],
+            name="test_string_split_basic",
+        )
+
+    @staticmethod
+    def export_maxsplit() -> None:
+        node = onnx.helper.make_node(
+            "StringSplit",
+            inputs=["x"],
+            outputs=["substrings", "length"],
+            maxsplit=2,
+        )
+
+        x = np.array(
+            [["hello world", "def.net"], ["o n n x", "the quick brown fox"]]
+        ).astype(object)
+
+        substrings = np.array(
+            [
+                [["hello", "world", ""], ["def.net", "", ""]],
+                [["o", "n", "n x"], ["the", "quick", "brown fox"]],
+            ]
+        ).astype(object)
+
+        length = np.array([[2, 1], [3, 3]], np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[substrings, length],
+            name="test_string_split_maxsplit",
+        )
+
+    @staticmethod
+    def export_consecutive_delimiters() -> None:
+        node = onnx.helper.make_node(
+            "StringSplit",
+            inputs=["x"],
+            outputs=["substrings", "length"],
+            delimiter="-",
+            maxsplit=None,
+        )
+
+        x = np.array(["o-n-n--x-", "o-n----nx"]).astype(object)
+
+        substrings = np.array(
+            [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]]
+        ).astype(object)
+
+        length = np.array([6, 6], dtype=np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[substrings, length],
+            name="test_string_split_consecutive_delimiters",
+        )
+
+    @staticmethod
+    def export_empty_string_delimiter() -> None:
+        for delimiter, test_name in (
+            ("", "test_string_split_empty_string_delimiter"),
+            (None, "test_string_split_no_delimiter"),
+        ):
+            node = onnx.helper.make_node(
+                "StringSplit",
+                inputs=["x"],
+                outputs=["substrings", "length"],
+                delimiter=delimiter,
+                maxsplit=None,
+            )
+
+            x = np.array(
+                ["hello world !", "  hello   world !", " hello world   ! "]
+            ).astype(object)
+
+            substrings = np.array(
+                [
+                    ["hello", "world", "!"],
+                    ["hello", "world", "!"],
+                    ["hello", "world", "!"],
+                ]
+            ).astype(object)
+
+            length = np.array([3, 3, 3], dtype=np.int64)
+
+            expect(
+                node,
+                inputs=[x],
+                outputs=[substrings, length],
+                name=test_name,
+            )
+
+    @staticmethod
+    def export_empty_string_split() -> None:
+        node = onnx.helper.make_node(
+            "StringSplit",
+            inputs=["x"],
+            outputs=["substrings", "length"],
+            delimiter=None,
+            maxsplit=None,
+        )
+
+        x = np.array([]).astype(object)
+
+        substrings = np.array([]).astype(object).reshape(0, 0)
+
+        length = np.array([], dtype=np.int64)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[substrings, length],
+            name="test_string_split_empty_tensor",
+            output_type_protos=[
+                onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, (0, None)),
+                None,
+            ],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stringnormalizer.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stringnormalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d945df61333e5ce82baf27894b97bdf829b76bec
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/stringnormalizer.py
@@ -0,0 +1,148 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class StringNormalizer(Base):
+    @staticmethod
+    def export_nostopwords_nochangecase() -> None:
+        input = np.array(["monday", "tuesday"]).astype(object)
+        output = input
+
+        # No stopwords. This is a NOOP
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            is_case_sensitive=1,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_nostopwords_nochangecase",
+        )
+
+    @staticmethod
+    def export_monday_casesensintive_nochangecase() -> None:
+        input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
+        stopwords = ["monday"]
+
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_export_monday_casesensintive_nochangecase",
+        )
+
+    @staticmethod
+    def export_monday_casesensintive_lower() -> None:
+        input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
+        stopwords = ["monday"]
+
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="LOWER",
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_export_monday_casesensintive_lower",
+        )
+
+    @staticmethod
+    def export_monday_casesensintive_upper() -> None:
+        input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
+        output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object)
+        stopwords = ["monday"]
+
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_export_monday_casesensintive_upper",
+        )
+
+    @staticmethod
+    def export_monday_empty_output() -> None:
+        input = np.array(["monday", "monday"]).astype(object)
+        output = np.array([""]).astype(object)
+        stopwords = ["monday"]
+
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            is_case_sensitive=1,
+            stopwords=stopwords,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_export_monday_empty_output",
+        )
+
+    @staticmethod
+    def export_monday_insensintive_upper_twodim() -> None:
+        input = (
+            np.array(
+                ["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"]
+            )
+            .astype(object)
+            .reshape([1, 6])
+        )
+
+        # It does upper case cecedille, accented E
+        # and german umlaut but fails
+        # with german eszett
+        output = (
+            np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"])
+            .astype(object)
+            .reshape([1, 4])
+        )
+        stopwords = ["monday"]
+
+        node = onnx.helper.make_node(
+            "StringNormalizer",
+            inputs=["x"],
+            outputs=["y"],
+            case_change_action="UPPER",
+            stopwords=stopwords,
+        )
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_strnormalizer_export_monday_insensintive_upper_twodim",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/swish.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/swish.py
new file mode 100644
index 0000000000000000000000000000000000000000..d08cd6338cc7ec31180d69931baa405335f88a47
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/swish.py
@@ -0,0 +1,36 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def swish(x: np.ndarray, alpha: float) -> np.ndarray:
+    return x * (1 / (1 + np.exp(-alpha * x)))
+
+
+class Swish(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Swish",
+            inputs=["x"],
+            outputs=["y"],
+            alpha=1.0,  # pass alpha as attribute
+        )
+
+        x = np.array([3, 4, 5], dtype=np.float32)
+        y = swish(x, alpha=1.0)
+
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_swish",
+            opset_imports=[onnx.helper.make_opsetid("", 24)],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tan.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tan.py
new file mode 100644
index 0000000000000000000000000000000000000000..393cb82b3c3c8d7762ce4b288aee51e9f02621ef
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tan.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Tan(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Tan",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.tan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tan_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.tan(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tan")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tanh.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tanh.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7926f35dc897132d9de28f8fe6e533923c6b09f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tanh.py
@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Tanh(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Tanh",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.array([-1, 0, 1]).astype(np.float32)
+        y = np.tanh(x)  # expected output [-0.76159418, 0., 0.76159418]
+        expect(node, inputs=[x], outputs=[y], name="test_tanh_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.tanh(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tanh")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tensorscatter.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tensorscatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e32c25bed592c7f2fe4632176f07e61a00608ae
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tensorscatter.py
@@ -0,0 +1,174 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class TensorScatter(Base):
+    @staticmethod
+    def export_tensorscatter() -> None:
+        node = onnx.helper.make_node(
+            "TensorScatter",
+            inputs=["past_cache", "update", "write_indices"],
+            outputs=["present_cache"],
+            mode="linear",
+        )
+        past_cache = np.array(
+            [
+                [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]],
+                [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]],
+            ],
+            dtype=np.float32,
+        )
+        update = np.array(
+            [
+                [[[5, 5, 5, 5, 5]]],
+                [[[1, 1, 1, 1, 1]]],
+            ],
+            dtype=np.float32,
+        )
+        write_indices = np.array([1, 2], dtype=np.int64)
+        present_cache = np.array(
+            [
+                [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]],
+                [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [1, 1, 1, 1, 1], [4, 3, 2, 1, 0]]],
+            ],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[past_cache, update, write_indices],
+            outputs=[present_cache],
+            name="test_tensorscatter",
+        )
+
+    @staticmethod
+    def export_tensorscatter_circular() -> None:
+        node = onnx.helper.make_node(
+            "TensorScatter",
+            inputs=["past_cache", "update", "write_indices"],
+            outputs=["present_cache"],
+            mode="circular",
+        )
+        past_cache = np.array(
+            [
+                [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]],
+                [[[1, 2, 3, 4, 5], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [4, 3, 2, 1, 0]]],
+            ],
+            dtype=np.float32,
+        )
+        update = np.array(
+            [
+                [
+                    [
+                        [5, 5, 5, 5, 5],
+                        [6, 6, 6, 6, 6],
+                    ]
+                ],
+                [
+                    [
+                        [1, 1, 1, 1, 1],
+                        [2, 2, 2, 2, 2],
+                    ]
+                ],
+            ],
+            dtype=np.float32,
+        )
+        write_indices = np.array([1, 3], dtype=np.int64)
+        present_cache = np.array(
+            [
+                [[[1, 2, 3, 4, 5], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [4, 3, 2, 1, 0]]],
+                [[[2, 2, 2, 2, 2], [5, 6, 7, 8, 9], [8, 7, 6, 5, 4], [1, 1, 1, 1, 1]]],
+            ],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[past_cache, update, write_indices],
+            outputs=[present_cache],
+            name="test_tensorscatter_circular",
+        )
+
+    @staticmethod
+    def export_tensorscatter_3d() -> None:
+        node = onnx.helper.make_node(
+            "TensorScatter",
+            inputs=["past_cache", "update", "write_indices"],
+            outputs=["present_cache"],
+        )
+        past_cache = np.array(
+            [
+                [
+                    [1, 2, 3, 4, 5],
+                    [5, 6, 7, 8, 9],
+                    [8, 7, 6, 5, 4],
+                    [5, 4, 3, 2, 1],
+                ],
+                [
+                    [1, 2, 3, 4, 5],
+                    [5, 6, 7, 8, 9],
+                    [8, 7, 6, 5, 4],
+                    [5, 4, 3, 2, 1],
+                ],
+                [
+                    [1, 2, 3, 4, 5],
+                    [5, 6, 7, 8, 9],
+                    [8, 7, 6, 5, 4],
+                    [5, 4, 3, 2, 1],
+                ],
+            ],
+            dtype=np.float32,
+        )
+        update = np.array(
+            [
+                [
+                    [4, 4, 4, 4, 4],
+                    [5, 5, 5, 5, 5],
+                ],
+                [
+                    [6, 6, 6, 6, 6],
+                    [7, 7, 7, 7, 7],
+                ],
+                [
+                    [2, 2, 2, 2, 2],
+                    [3, 3, 3, 3, 3],
+                ],
+            ],
+            dtype=np.float32,
+        )
+        write_indices = np.array([1, 2, 0], dtype=np.int64)
+        present_cache = np.array(
+            [
+                [
+                    [1, 2, 3, 4, 5],
+                    [4, 4, 4, 4, 4],
+                    [5, 5, 5, 5, 5],
+                    [5, 4, 3, 2, 1],
+                ],
+                [
+                    [1, 2, 3, 4, 5],
+                    [5, 6, 7, 8, 9],
+                    [6, 6, 6, 6, 6],
+                    [7, 7, 7, 7, 7],
+                ],
+                [
+                    [2, 2, 2, 2, 2],
+                    [3, 3, 3, 3, 3],
+                    [8, 7, 6, 5, 4],
+                    [5, 4, 3, 2, 1],
+                ],
+            ],
+            dtype=np.float32,
+        )
+        expect(
+            node,
+            inputs=[past_cache, update, write_indices],
+            outputs=[present_cache],
+            name="test_tensorscatter_3d",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tfidfvectorizer.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tfidfvectorizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0c67025ff578ee48a27c8f7620e40d04ca9da9
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tfidfvectorizer.py
@@ -0,0 +1,264 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+import onnx
+from onnx import NodeProto
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class TfIdfVectorizerHelper:
+    def __init__(self, **params: Any) -> None:
+        # Attr names
+        mode = "mode"
+        min_gram_length = "min_gram_length"
+        max_gram_length = "max_gram_length"
+        max_skip_count = "max_skip_count"
+        ngram_counts = "ngram_counts"
+        ngram_indexes = "ngram_indexes"
+        pool_int64s = "pool_int64s"
+
+        required_attr = [
+            mode,
+            min_gram_length,
+            max_gram_length,
+            max_skip_count,
+            ngram_counts,
+            ngram_indexes,
+            pool_int64s,
+        ]
+
+        for i in required_attr:
+            assert i in params, f"Missing attribute: {i}"
+
+        self.mode = params[mode]
+        self.min_gram_length = params[min_gram_length]
+        self.max_gram_length = params[max_gram_length]
+        self.max_skip_count = params[max_skip_count]
+        self.ngram_counts = params[ngram_counts]
+        self.ngram_indexes = params[ngram_indexes]
+        self.pool_int64s = params[pool_int64s]
+
+    def make_node_noweights(self) -> NodeProto:
+        return onnx.helper.make_node(
+            "TfIdfVectorizer",
+            inputs=["X"],
+            outputs=["Y"],
+            mode=self.mode,
+            min_gram_length=self.min_gram_length,
+            max_gram_length=self.max_gram_length,
+            max_skip_count=self.max_skip_count,
+            ngram_counts=self.ngram_counts,
+            ngram_indexes=self.ngram_indexes,
+            pool_int64s=self.pool_int64s,
+        )
+
+
+class TfIdfVectorizer(Base):
+    @staticmethod
+    def export_tf_only_bigrams_skip0() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_only_bigrams_skip0",
+        )
+
+    @staticmethod
+    def export_tf_batch_onlybigrams_skip0() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array(
+            [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]]
+        ).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0",
+        )
+
+    @staticmethod
+    def export_tf_onlybigrams_levelempty() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([1.0, 1.0, 1.0]).astype(np.float32)
+
+        ngram_counts = np.array([0, 0]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
+        pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype(  # unigrams none
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=0,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_onlybigrams_levelempty",
+        )
+
+    @staticmethod
+    def export_tf_onlybigrams_skip5() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_onlybigrams_skip5",
+        )
+
+    @staticmethod
+    def export_tf_batch_onlybigrams_skip5() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array(
+            [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]]
+        ).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=2,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5",
+        )
+
+    @staticmethod
+    def export_tf_uniandbigrams_skip5() -> None:
+        input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
+        output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=1,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_uniandbigrams_skip5",
+        )
+
+    @staticmethod
+    def export_tf_batch_uniandbigrams_skip5() -> None:
+        input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
+        output = np.array(
+            [[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]]
+        ).astype(np.float32)
+
+        ngram_counts = np.array([0, 4]).astype(np.int64)
+        ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
+        pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype(  # unigrams
+            np.int64
+        )  # bigrams
+
+        helper = TfIdfVectorizerHelper(
+            mode="TF",
+            min_gram_length=1,
+            max_gram_length=2,
+            max_skip_count=5,
+            ngram_counts=ngram_counts,
+            ngram_indexes=ngram_indexes,
+            pool_int64s=pool_int64s,
+        )
+        node = helper.make_node_noweights()
+        expect(
+            node,
+            inputs=[input],
+            outputs=[output],
+            name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/thresholdedrelu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/thresholdedrelu.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f3fc6c2055aa2f57c8689a762d92139955df674
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/thresholdedrelu.py
@@ -0,0 +1,41 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class ThresholdedRelu(Base):
+    @staticmethod
+    def export() -> None:
+        alpha = 2.0
+        node = onnx.helper.make_node(
+            "ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha
+        )
+
+        x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32)
+        y = np.clip(x, alpha, np.inf)  # expected output [0., 0., 0., 0., 2.2]
+        y[y == alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example")
+
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, alpha, np.inf)
+        y[y == alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu")
+
+    @staticmethod
+    def export_default() -> None:
+        default_alpha = 1.0
+        node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"])
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        y = np.clip(x, default_alpha, np.inf)
+        y[y == default_alpha] = 0
+
+        expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tile.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tile.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55555c079d498f3ca9a30e938cecc000aab11e4
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/tile.py
@@ -0,0 +1,38 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Tile(Base):
+    @staticmethod
+    def export_tile() -> None:
+        node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
+
+        x = np.random.rand(2, 3, 4, 5).astype(np.float32)
+
+        repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
+
+        z = np.tile(x, repeats)
+
+        expect(node, inputs=[x, repeats], outputs=[z], name="test_tile")
+
+    @staticmethod
+    def export_tile_precomputed() -> None:
+        node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
+
+        x = np.array([[0, 1], [2, 3]], dtype=np.float32)
+
+        repeats = np.array([2, 2], dtype=np.int64)
+
+        z = np.array(
+            [[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32
+        )
+
+        expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/topk.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/topk.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a14a5a173e27a0386aa050acadc2b3d37719f72
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/topk.py
@@ -0,0 +1,262 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def topk_sorted_implementation(X, k, axis, largest):
+    ind_axis = np.indices(X.shape)[axis]
+    if largest:
+        ind_axis = -ind_axis
+    sorted_indices = np.lexsort((ind_axis, X), axis=axis)
+    sorted_values = np.sort(X, axis=axis)
+    if largest:
+        sorted_indices = np.flip(sorted_indices, axis=axis)
+        sorted_values = np.flip(sorted_values, axis=axis)
+    topk_sorted_indices = np.take(sorted_indices, np.arange(k), axis=axis)
+    topk_sorted_values = np.take(sorted_values, np.arange(k), axis=axis)
+    return topk_sorted_values, np.array(topk_sorted_indices, dtype=np.int64)
+
+
+class TopK(Base):
+    @staticmethod
+    def export_top_k() -> None:
+        axis = 1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [
+                [0, 1, 2, 3],
+                [4, 5, 6, 7],
+                [8, 9, 10, 11],
+            ],
+            dtype=np.float32,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [[ 3.  2.  1.]
+        # [ 7.  6.  5.]
+        # [11. 10.  9.]]
+        # print(indices_ref)
+        # [[3 2 1]
+        # [3 2 1]
+        # [3 2 1]]
+
+        expect(
+            node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k"
+        )
+
+    @staticmethod
+    def export_top_k_uint64() -> None:
+        axis = 1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [
+                [0, 1, 2, 3],
+                [4, 5, 6, 7],
+                [8, 9, 10, 11],
+            ],
+            dtype=np.uint64,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [[ 3  2  1]
+        # [ 7  6  5]
+        # [11 10  9]]
+        # print(indices_ref)
+        # [[3 2 1]
+        # [3 2 1]
+        # [3 2 1]]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_uint64",
+        )
+
+    @staticmethod
+    def export_top_k_same_values() -> None:
+        axis = 0
+        largest = 0
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [0, 0, 0, 0],
+            dtype=np.int64,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # (Pdb) print(values_ref)
+        # [0 0 0]
+        # (Pdb) print(indices_ref)
+        # [0 1 2]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_same_values",
+        )
+
+    @staticmethod
+    def export_top_k_same_values_largest() -> None:
+        axis = 0
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [0, 0, 0, 0],
+            dtype=np.int64,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [0 0 0]
+        # print(indices_ref)
+        # [0 1 2]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_same_values_largest",
+        )
+
+    @staticmethod
+    def export_top_k_same_values_2d() -> None:
+        axis = 1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 1, 1]],
+            dtype=np.int64,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [[0 0 0]
+        # [1 1 1]
+        # [1 1 2]]
+        # print(indices_ref)
+        # [[0 1 2]
+        # [0 1 2]
+        # [2 3 0]]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_same_values_2d",
+        )
+
+    @staticmethod
+    def export_top_k_smallest() -> None:
+        axis = 1
+        largest = 0
+        sorted = 1  # noqa: A001
+        k = 3
+
+        node = onnx.helper.make_node(
+            "TopK",
+            inputs=["x", "k"],
+            outputs=["values", "indices"],
+            axis=axis,
+            largest=largest,
+            sorted=sorted,
+        )
+
+        X = np.array(
+            [
+                [0, 1, 2, 3],
+                [4, 5, 6, 7],
+                [11, 10, 9, 8],
+            ],
+            dtype=np.float32,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [[ 0.  1.  2.]
+        # [ 4.  5.  6.]
+        # [ 8.  9. 10.]]
+        # print(indices_ref)
+        # [[0 1 2]
+        # [0 1 2]
+        # [3 2 1]]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_smallest",
+        )
+
+    @staticmethod
+    def export_top_k_negative_axis() -> None:
+        axis = -1
+        largest = 1
+
+        k = 3
+        node = onnx.helper.make_node(
+            "TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
+        )
+        X = np.array(
+            [
+                [0, 1, 2, 3],
+                [4, 5, 6, 7],
+                [8, 9, 10, 11],
+            ],
+            dtype=np.float32,
+        )
+        K = np.array([k], dtype=np.int64)
+        values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
+
+        # print(values_ref)
+        # [[ 3.  2.  1.]
+        # [ 7.  6.  5.]
+        # [11. 10.  9.]]
+        # print(indices_ref)
+        # [[3 2 1]
+        # [3 2 1]
+        # [3 2 1]]
+
+        expect(
+            node,
+            inputs=[X, K],
+            outputs=[values_ref, indices_ref],
+            name="test_top_k_negative_axis",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/transpose.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/transpose.py
new file mode 100644
index 0000000000000000000000000000000000000000..95fbdc042eaf59145d64c488ade5aa7f754a8ce8
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/transpose.py
@@ -0,0 +1,47 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Transpose(Base):
+    @staticmethod
+    def export_default() -> None:
+        shape = (2, 3, 4)
+        data = np.random.random_sample(shape).astype(np.float32)
+
+        node = onnx.helper.make_node(
+            "Transpose", inputs=["data"], outputs=["transposed"]
+        )
+
+        transposed = np.transpose(data)
+        expect(node, inputs=[data], outputs=[transposed], name="test_transpose_default")
+
+    @staticmethod
+    def export_all_permutations() -> None:
+        shape = (2, 3, 4)
+        data = np.random.random_sample(shape).astype(np.float32)
+        permutations = list(itertools.permutations(np.arange(len(shape))))
+
+        for i, permutation in enumerate(permutations):
+            node = onnx.helper.make_node(
+                "Transpose",
+                inputs=["data"],
+                outputs=["transposed"],
+                perm=permutation,
+            )
+            transposed = np.transpose(data, permutation)
+            expect(
+                node,
+                inputs=[data],
+                outputs=[transposed],
+                name=f"test_transpose_all_permutations_{i}",
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/trilu.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/trilu.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9443c3e08edb895475eedccedd9218641cb8aca
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/trilu.py
@@ -0,0 +1,453 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def triu_reference_implementation(x, k=0):
+    return np.triu(x, k)
+
+
+def tril_reference_implementation(x, k=0):
+    return np.tril(x, k)
+
+
+class Trilu(Base):
+    @staticmethod
+    def export_triu() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [0, 2, 8, 6, 9],
+        #   [0, 0, 0, 8, 7],
+        #   [0, 0, 0, 2, 4]]
+        y = triu_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name="test_triu")
+
+    @staticmethod
+    def export_triu_neg() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [0, 4, 0, 8, 7],
+        #   [0, 0, 4, 2, 4]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_neg")
+
+    @staticmethod
+    def export_triu_out_neg_out() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-7).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_neg_out")
+
+    @staticmethod
+    def export_triu_pos() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(2).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 3, 7, 9],
+        #   [0, 0, 0, 6, 9],
+        #   [0, 0, 0, 0, 7],
+        #   [0, 0, 0, 0, 0]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_pos")
+
+    @staticmethod
+    def export_triu_out_pos() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 0, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_out_pos")
+
+    @staticmethod
+    def export_triu_square() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        y = triu_reference_implementation(x)
+        # X:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [8, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [8, 9, 8]]]
+        # expect result:
+        # [[[4, 6, 9],
+        #   [0, 5, 4],
+        #   [0, 0, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [0, 6, 3],
+        #   [0, 0, 8]]]
+        expect(node, inputs=[x], outputs=[y], name="test_triu_square")
+
+    @staticmethod
+    def export_triu_square_neg() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [8, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [8, 9, 8]]]
+        # expect result:
+        # [[[4, 6, 9],
+        #   [7, 5, 4],
+        #   [0, 1, 2]],
+        #
+        #  [[1, 4, 9],
+        #   [9, 6, 3],
+        #   [0, 9, 8]]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_square_neg")
+
+    @staticmethod
+    def export_triu_one_row() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+        k = np.array(1).astype(np.int64)
+        # X:
+        # [[[1, 4, 9, 7, 1]],
+        #
+        #  [[9, 2, 8, 8, 4]],
+        #
+        #  [[3, 9, 7, 4, 2]]]
+        # expect result:
+        # [[[0, 4, 9, 7, 1]],
+        #
+        #  [[0, 2, 8, 8, 4]],
+        #
+        #  [[0, 9, 7, 4, 2]]]
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_one_row")
+
+    @staticmethod
+    def export_triu_zero() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+        )
+
+        x = np.random.randint(10, size=(0, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        # []
+        # expect result:
+        # []
+        y = triu_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_triu_zero")
+
+    @staticmethod
+    def export_tril() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 0, 0, 0, 0],
+        #   [1, 2, 0, 0, 0],
+        #   [9, 4, 1, 0, 0],
+        #   [4, 3, 4, 2, 0]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tril")
+
+    @staticmethod
+    def export_tril_neg() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [1, 0, 0, 0, 0],
+        #   [9, 4, 0, 0, 0],
+        #   [4, 3, 4, 0, 0]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_neg")
+
+    @staticmethod
+    def export_tril_out_neg() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(-7).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0],
+        #   [0, 0, 0, 0, 0]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_neg")
+
+    @staticmethod
+    def export_tril_pos() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(2).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 0, 0],
+        #   [1, 2, 8, 6, 0],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_pos")
+
+    @staticmethod
+    def export_tril_out_pos() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+        x = np.random.randint(10, size=(4, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        # expect result:
+        #  [[4, 7, 3, 7, 9],
+        #   [1, 2, 8, 6, 9],
+        #   [9, 4, 1, 8, 7],
+        #   [4, 3, 4, 2, 4]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_out_pos")
+
+    @staticmethod
+    def export_tril_square() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        # X:
+        # [[[0, 4, 3],
+        #   [2, 0, 9],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 7, 2],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        # expect result:
+        # [[[0, 0, 0],
+        #   [2, 0, 0],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 0, 0],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tril_square")
+
+    @staticmethod
+    def export_tril_square_neg() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(2, 3, 3)).astype(np.int64)
+        k = np.array(-1).astype(np.int64)
+        # X:
+        # [[[0, 4, 3],
+        #   [2, 0, 9],
+        #   [8, 2, 5]],
+        #
+        #  [[2, 7, 2],
+        #   [2, 6, 0],
+        #   [2, 6, 5]]]
+        # expect result:
+        # [[[0, 0, 0],
+        #   [2, 0, 0],
+        #   [8, 2, 0]],
+        #
+        #  [[0, 0, 0],
+        #   [2, 0, 0],
+        #   [2, 6, 0]]]
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_square_neg")
+
+    @staticmethod
+    def export_tril_one_row() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(3, 1, 5)).astype(np.int64)
+        # X:
+        # [[[6, 2, 4, 1, 6]],
+        #
+        #  [[8, 3, 8, 7, 0]],
+        #
+        #  [[2, 2, 9, 5, 9]]]
+        # expect result:
+        # [[[6, 0, 0, 0, 0]],
+        #
+        #  [[8, 0, 0, 0, 0]],
+        #
+        #  [[2, 0, 0, 0, 0]]]
+        y = tril_reference_implementation(x)
+        expect(node, inputs=[x], outputs=[y], name="test_tril_one_row_neg")
+
+    @staticmethod
+    def export_tril_zero() -> None:
+        node = onnx.helper.make_node(
+            "Trilu",
+            inputs=["x", "k"],
+            outputs=["y"],
+            upper=0,
+        )
+
+        x = np.random.randint(10, size=(3, 0, 5)).astype(np.int64)
+        k = np.array(6).astype(np.int64)
+        # X:
+        # []
+        # expect result:
+        # []
+        y = tril_reference_implementation(x, int(k))
+        expect(node, inputs=[x, k], outputs=[y], name="test_tril_zero")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unique.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unique.py
new file mode 100644
index 0000000000000000000000000000000000000000..2827f57563b71d8762cf4bd736333a1c78943ff5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unique.py
@@ -0,0 +1,229 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def specify_int64(indices, inverse_indices, counts):
+    return (
+        np.array(indices, dtype=np.int64),
+        np.array(inverse_indices, dtype=np.int64),
+        np.array(counts, dtype=np.int64),
+    )
+
+
+class Unique(Base):
+    @staticmethod
+    def export_sorted_without_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+        )
+
+        x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        expect(
+            node_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_sorted_without_axis",
+        )
+
+    @staticmethod
+    def export_not_sorted_without_axis() -> None:
+        node_not_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+            sorted=0,
+        )
+        # numpy unique does not retain original order (it sorts the output unique values)
+        # https://github.com/numpy/numpy/issues/8621
+        # we need to recover unsorted output and indices
+        x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+
+        # prepare index mapping from sorted to unsorted
+        argsorted_indices = np.argsort(indices)
+        inverse_indices_map = dict(
+            zip(argsorted_indices, np.arange(len(argsorted_indices)))
+        )
+
+        indices = indices[argsorted_indices]
+        y = np.take(x, indices, axis=0)
+        inverse_indices = np.asarray(
+            [inverse_indices_map[i] for i in inverse_indices], dtype=np.int64
+        )
+        counts = counts[argsorted_indices]
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        # print(y)
+        # [2.0, 1.0, 3.0, 4.0]
+        # print(indices)
+        # [0 1 3 4]
+        # print(inverse_indices)
+        # [0, 1, 1, 2, 3, 2]
+        # print(counts)
+        # [1, 2, 2, 1]
+
+        expect(
+            node_not_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_not_sorted_without_axis",
+        )
+
+    @staticmethod
+    def export_sorted_with_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+            sorted=1,
+            axis=0,
+        )
+
+        x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        # behavior changed with numpy >= 2.0
+        inverse_indices = inverse_indices.reshape(-1)
+        # print(y)
+        # [[1. 0. 0.]
+        #  [2. 3. 4.]]
+        # print(indices)
+        # [0 2]
+        # print(inverse_indices)
+        # [0 0 1]
+        # print(counts)
+        # [2 1]
+
+        expect(
+            node_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_sorted_with_axis",
+        )
+
+    @staticmethod
+    def export_sorted_with_axis_3d() -> None:
+        node_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+            sorted=1,
+            axis=1,
+        )
+
+        x = np.array(
+            [
+                [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
+                [[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
+            ],
+            dtype=np.float32,
+        )
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        # behavior changed with numpy >= 2.0
+        inverse_indices = inverse_indices.reshape(-1)
+        # print(y)
+        # [[[0. 1.]
+        #  [1. 1.]
+        #  [2. 1.]]
+        # [[0. 1.]
+        #  [1. 1.]
+        #  [2. 1.]]]
+        # print(indices)
+        # [1 0 2]
+        # print(inverse_indices)
+        # [1 0 2 0]
+        # print(counts)
+        # [2 1 1]
+        expect(
+            node_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_sorted_with_axis_3d",
+        )
+
+    @staticmethod
+    def export_sorted_with_negative_axis() -> None:
+        node_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+            sorted=1,
+            axis=-1,
+        )
+
+        x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        # behavior changed with numpy >= 2.0
+        inverse_indices = inverse_indices.reshape(-1)
+        # print(y)
+        # [[0. 1.]
+        #  [0. 1.]
+        #  [3. 2.]]
+        # print(indices)
+        # [1 0]
+        # print(inverse_indices)
+        # [1 0 0]
+        # print(counts)
+        # [2 1]
+
+        expect(
+            node_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_sorted_with_negative_axis",
+        )
+
+    @staticmethod
+    def export_length_1() -> None:
+        node_sorted = onnx.helper.make_node(
+            "Unique",
+            inputs=["X"],
+            outputs=["Y", "indices", "inverse_indices", "counts"],
+            sorted=1,
+        )
+
+        x = np.array([0], dtype=np.int64)
+        y, indices, inverse_indices, counts = np.unique(x, True, True, True)
+        indices, inverse_indices, counts = specify_int64(
+            indices, inverse_indices, counts
+        )
+        # behavior changed with numpy >= 2.0
+        inverse_indices = inverse_indices.reshape(-1)
+        # print(y)
+        # [0]
+        # print(indices)
+        # [0]
+        # print(inverse_indices)
+        # [0]
+        # print(counts)
+        # [1]
+
+        expect(
+            node_sorted,
+            inputs=[x],
+            outputs=[y, indices, inverse_indices, counts],
+            name="test_unique_length_1",
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unsqueeze.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unsqueeze.py
new file mode 100644
index 0000000000000000000000000000000000000000..d52284486740c69014ab70fd196ad84c261c3cfb
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/unsqueeze.py
@@ -0,0 +1,91 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Unsqueeze(Base):
+    @staticmethod
+    def export_unsqueeze_one_axis() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+
+        for i in range(x.ndim):
+            axes = np.array([i]).astype(np.int64)
+            node = onnx.helper.make_node(
+                "Unsqueeze",
+                inputs=["x", "axes"],
+                outputs=["y"],
+            )
+            y = np.expand_dims(x, axis=i)
+
+            expect(
+                node,
+                inputs=[x, axes],
+                outputs=[y],
+                name="test_unsqueeze_axis_" + str(i),
+            )
+
+    @staticmethod
+    def export_unsqueeze_two_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([1, 4]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            "Unsqueeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        y = np.expand_dims(x, axis=1)
+        y = np.expand_dims(y, axis=4)
+
+        expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_two_axes")
+
+    @staticmethod
+    def export_unsqueeze_three_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([2, 4, 5]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            "Unsqueeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        y = np.expand_dims(x, axis=2)
+        y = np.expand_dims(y, axis=4)
+        y = np.expand_dims(y, axis=5)
+
+        expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_three_axes")
+
+    @staticmethod
+    def export_unsqueeze_unsorted_axes() -> None:
+        x = np.random.randn(3, 4, 5).astype(np.float32)
+        axes = np.array([5, 4, 2]).astype(np.int64)
+
+        node = onnx.helper.make_node(
+            "Unsqueeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        y = np.expand_dims(x, axis=2)
+        y = np.expand_dims(y, axis=4)
+        y = np.expand_dims(y, axis=5)
+
+        expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_unsorted_axes")
+
+    @staticmethod
+    def export_unsqueeze_negative_axes() -> None:
+        node = onnx.helper.make_node(
+            "Unsqueeze",
+            inputs=["x", "axes"],
+            outputs=["y"],
+        )
+        x = np.random.randn(1, 3, 1, 5).astype(np.float32)
+        axes = np.array([-2]).astype(np.int64)
+        y = np.expand_dims(x, axis=-2)
+        expect(node, inputs=[x, axes], outputs=[y], name="test_unsqueeze_negative_axes")
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/upsample.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/upsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..99e9d5f3f10ca1e3a7d447698f671bca48dc1587
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/upsample.py
@@ -0,0 +1,58 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx import helper
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Upsample(Base):
+    @staticmethod
+    def export_nearest() -> None:
+        node = onnx.helper.make_node(
+            "Upsample",
+            inputs=["X", "scales"],
+            outputs=["Y"],
+            mode="nearest",
+        )
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+        output = np.array(
+            [
+                [
+                    [
+                        [1, 1, 1, 2, 2, 2],
+                        [1, 1, 1, 2, 2, 2],
+                        [3, 3, 3, 4, 4, 4],
+                        [3, 3, 3, 4, 4, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        expect(
+            node,
+            inputs=[data, scales],
+            outputs=[output],
+            name="test_upsample_nearest",
+            opset_imports=[helper.make_opsetid("", 9)],
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/where.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/where.py
new file mode 100644
index 0000000000000000000000000000000000000000..87ec32f481113fac1784acee9997245e2d53eb2d
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/node/where.py
@@ -0,0 +1,42 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+class Where(Base):
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node(
+            "Where",
+            inputs=["condition", "x", "y"],
+            outputs=["z"],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        x = np.array([[1, 2], [3, 4]], dtype=np.float32)
+        y = np.array([[9, 8], [7, 6]], dtype=np.float32)
+        z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+        expect(node, inputs=[condition, x, y], outputs=[z], name="test_where_example")
+
+    @staticmethod
+    def export_long() -> None:
+        node = onnx.helper.make_node(
+            "Where",
+            inputs=["condition", "x", "y"],
+            outputs=["z"],
+        )
+
+        condition = np.array([[1, 0], [1, 1]], dtype=bool)
+        x = np.array([[1, 2], [3, 4]], dtype=np.int64)
+        y = np.array([[9, 8], [7, 6]], dtype=np.int64)
+        z = np.where(condition, x, y)  # expected output [[1, 8], [3, 4]]
+        expect(
+            node, inputs=[condition, x, y], outputs=[z], name="test_where_long_example"
+        )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/test_case.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/test_case.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ef94220f105c30442b4c248dc0fae6d19488642
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/test_case.py
@@ -0,0 +1,29 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    import numpy as np
+
+    import onnx
+
+
+@dataclass
+class TestCase:
+    name: str
+    model_name: str
+    url: str | None
+    model_dir: str | None
+    model: onnx.ModelProto | None
+    data_sets: Sequence[tuple[Sequence[np.ndarray], Sequence[np.ndarray]]] | None
+    kind: str
+    rtol: float
+    atol: float
+    # Tell PyTest this isn't a real test.
+    __test__: bool = False
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/utils.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff754be4333d625d4ae5923eeff949c05735e670
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/case/utils.py
@@ -0,0 +1,45 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import importlib
+import pkgutil
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from onnx import ONNX_ML
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+all_numeric_dtypes = [
+    np.int8,
+    np.int16,
+    np.int32,
+    np.int64,
+    np.uint8,
+    np.uint16,
+    np.uint32,
+    np.uint64,
+    np.float16,
+    np.float32,
+    np.float64,
+]
+
+
+def import_recursive(package: ModuleType) -> None:
+    """Takes a package and imports all modules underneath it."""
+    pkg_dir = package.__path__
+    module_location = package.__name__
+    for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir):
+        module_name = f"{module_location}.{name}"  # Module/package
+        if not ONNX_ML and module_name.startswith(
+            "onnx.backend.test.case.node.ai_onnx_ml"
+        ):
+            continue
+
+        module = importlib.import_module(module_name)
+        if ispkg:
+            import_recursive(module)
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/cmd_tools.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/cmd_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b004bb28e825a2e90760c460bbaed031c51e924
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/cmd_tools.py
@@ -0,0 +1,190 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import warnings
+
+import onnx.backend.test.case.model as model_test
+import onnx.backend.test.case.node as node_test
+from onnx import ONNX_ML, TensorProto, numpy_helper
+
+TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+DATA_DIR = os.path.join(TOP_DIR, "data")
+
+
+def generate_data(args: argparse.Namespace) -> None:
+    def prepare_dir(path: str) -> None:
+        if os.path.exists(path):
+            shutil.rmtree(path)
+        os.makedirs(path)
+
+    # Clean the output directory before generating data for node testcases
+    # It is used to check new generated data is correct in CIs
+    node_root = os.path.join(args.output, "node")
+    original_dir_number = len(
+        [name for name in os.listdir(node_root) if os.path.isfile(name)]
+    )
+    if args.clean and os.path.exists(node_root):
+        for sub_dir in os.listdir(node_root):
+            if ONNX_ML or not sub_dir.startswith("test_ai_onnx_ml_"):
+                shutil.rmtree(os.path.join(node_root, sub_dir))
+
+    cases = model_test.collect_testcases()
+    # If op_type is specified, only include those testcases including the given operator
+    # Otherwise, include all of the testcases
+    if args.diff:
+        cases += node_test.collect_diff_testcases()
+    else:
+        cases += node_test.collect_testcases(args.op_type)
+    node_number = 0
+
+    for case in cases:
+        output_dir = os.path.join(args.output, case.kind, case.name)
+        prepare_dir(output_dir)
+        if case.kind == "node":
+            node_number += 1
+        if case.kind == "real":
+            with open(os.path.join(output_dir, "data.json"), "w") as fi:
+                json.dump(
+                    {
+                        "url": case.url,
+                        "model_name": case.model_name,
+                        "rtol": case.rtol,
+                        "atol": case.atol,
+                    },
+                    fi,
+                    sort_keys=True,
+                )
+        else:
+            assert case.model
+            with open(os.path.join(output_dir, "model.onnx"), "wb") as f:
+                f.write(case.model.SerializeToString())
+            assert case.data_sets
+            for i, (inputs, outputs) in enumerate(case.data_sets):
+                data_set_dir = os.path.join(output_dir, f"test_data_set_{i}")
+                prepare_dir(data_set_dir)
+                for j, input in enumerate(inputs):
+                    with open(os.path.join(data_set_dir, f"input_{j}.pb"), "wb") as f:
+                        if case.model.graph.input[j].type.HasField("map_type"):
+                            f.write(
+                                numpy_helper.from_dict(
+                                    input, case.model.graph.input[j].name
+                                ).SerializeToString()
+                            )
+                        elif case.model.graph.input[j].type.HasField("sequence_type"):
+                            f.write(
+                                numpy_helper.from_list(
+                                    input, case.model.graph.input[j].name
+                                ).SerializeToString()
+                            )
+                        elif case.model.graph.input[j].type.HasField("optional_type"):
+                            f.write(
+                                numpy_helper.from_optional(
+                                    input, case.model.graph.input[j].name
+                                ).SerializeToString()
+                            )
+                        else:
+                            assert case.model.graph.input[j].type.HasField(
+                                "tensor_type"
+                            )
+                            if isinstance(input, TensorProto):
+                                f.write(input.SerializeToString())
+                            else:
+                                f.write(
+                                    numpy_helper.from_array(
+                                        input, case.model.graph.input[j].name
+                                    ).SerializeToString()
+                                )
+                for j, output in enumerate(outputs):
+                    with open(os.path.join(data_set_dir, f"output_{j}.pb"), "wb") as f:
+                        if case.model.graph.output[j].type.HasField("map_type"):
+                            f.write(
+                                numpy_helper.from_dict(
+                                    output, case.model.graph.output[j].name
+                                ).SerializeToString()
+                            )
+                        elif case.model.graph.output[j].type.HasField("sequence_type"):
+                            f.write(
+                                numpy_helper.from_list(
+                                    output, case.model.graph.output[j].name
+                                ).SerializeToString()
+                            )
+                        elif case.model.graph.output[j].type.HasField("optional_type"):
+                            f.write(
+                                numpy_helper.from_optional(
+                                    output, case.model.graph.output[j].name
+                                ).SerializeToString()
+                            )
+                        else:
+                            assert case.model.graph.output[j].type.HasField(
+                                "tensor_type"
+                            )
+                            if isinstance(output, TensorProto):
+                                f.write(output.SerializeToString())
+                            else:
+                                f.write(
+                                    numpy_helper.from_array(
+                                        output, case.model.graph.output[j].name
+                                    ).SerializeToString()
+                                )
+    if not args.clean and node_number != original_dir_number:
+        warnings.warn(
+            "There are some models under 'onnx/backend/test/data/node' which cannot not"
+            " be generated by the script from 'onnx/backend/test/case/node'. Please add"
+            " '--clean' option for 'python onnx/backend/test/cmd_tools.py generate-data'"
+            " to cleanup the existing directories and regenerate them.",
+            Warning,
+            stacklevel=2,
+        )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser("backend-test-tools")
+    subparsers = parser.add_subparsers()
+
+    subparser = subparsers.add_parser(
+        "generate-data", help="convert testcases to test data."
+    )
+    subparser.add_argument(
+        "-c",
+        "--clean",
+        default=False,
+        action="store_true",
+        help="Clean the output directory before generating data for node testcases.",
+    )
+    subparser.add_argument(
+        "-o",
+        "--output",
+        default=DATA_DIR,
+        help="output directory (default: %(default)s)",
+    )
+    subparser.add_argument(
+        "-t",
+        "--op_type",
+        default=None,
+        help="op_type for test case generation. (generates test data for the specified op_type only.)",
+    )
+    subparser.add_argument(
+        "-d",
+        "--diff",
+        default=False,
+        action="store_true",
+        help="only generates test data for those changed files (compared to the main branch).",
+    )
+    subparser.set_defaults(func=generate_data)
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    args.func(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/README.md b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..787c0a3dcc213a92da75b5109335ec0dd3338a66
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/README.md
@@ -0,0 +1,16 @@
+
+
+# Light models
+
+The models in this folder were created by replacing
+all float initializers by nodes `ConstantOfShape`
+with function `replace_initializer_by_constant_of_shape`.
+The models are lighter and can be added to the repository
+for unit testing.
+
+Expected outputs were obtained by using CReferenceEvaluator
+implemented in [PR 4952](https://github.com/onnx/onnx/pull/4952).
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/light_shufflenet.onnx b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/light_shufflenet.onnx
new file mode 100644
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--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/light/light_shufflenet.onnx
@@ -0,0 +1,3 @@
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/node/test_abs/model.onnx b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/node/test_abs/model.onnx
new file mode 100644
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+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/data/node/test_abs/model.onnx
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diff --git a/pythonProject/.venv/Lib/site-packages/onnx/backend/test/stat_coverage.py b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/stat_coverage.py
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+++ b/pythonProject/.venv/Lib/site-packages/onnx/backend/test/stat_coverage.py
@@ -0,0 +1,295 @@
+#!/usr/bin/env python
+
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import os
+from typing import IO, TYPE_CHECKING, Any
+
+from onnx import AttributeProto, defs, load
+from onnx.backend.test.case import collect_snippets
+from onnx.backend.test.loader import load_model_tests
+from onnx.backend.test.runner import Runner
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+def is_ml(schemas: Sequence[defs.OpSchema]) -> bool:
+    return any(s.domain == "ai.onnx.ml" for s in schemas)
+
+
+def gen_outlines(f: IO[Any], ml: bool) -> None:
+    f.write("# Test Coverage Report")
+    if ml:
+        f.write(" (ONNX-ML Operators)\n")
+    else:
+        f.write(" (ONNX Core Operators)\n")
+    f.write("## Outlines\n")
+    f.write("* [Node Test Coverage](#node-test-coverage)\n")
+    f.write("* [Model Test Coverage](#model-test-coverage)\n")
+    f.write("* [Overall Test Coverage](#overall-test-coverage)\n")
+
+
+common_covered: Sequence[str] = []
+experimental_covered: Sequence[str] = []
+
+
+def gen_node_test_coverage(
+    schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
+) -> None:
+    global common_covered  # noqa: PLW0603
+    global experimental_covered  # noqa: PLW0603
+    generators = set(
+        {
+            "Multinomial",
+            "RandomNormal",
+            "RandomNormalLike",
+            "RandomUniform",
+            "RandomUniformLike",
+        }
+    )
+    node_tests = collect_snippets()
+    common_covered = sorted(
+        s.name
+        for s in schemas
+        if s.name in node_tests
+        and s.support_level == defs.OpSchema.SupportType.COMMON
+        and (s.domain == "ai.onnx.ml") == ml
+    )
+    common_no_cover = sorted(
+        s.name
+        for s in schemas
+        if s.name not in node_tests
+        and s.support_level == defs.OpSchema.SupportType.COMMON
+        and (s.domain == "ai.onnx.ml") == ml
+    )
+    common_generator = sorted(name for name in common_no_cover if name in generators)
+    experimental_covered = sorted(
+        s.name
+        for s in schemas
+        if s.name in node_tests
+        and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
+        and (s.domain == "ai.onnx.ml") == ml
+    )
+    experimental_no_cover = sorted(
+        s.name
+        for s in schemas
+        if s.name not in node_tests
+        and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
+        and (s.domain == "ai.onnx.ml") == ml
+    )
+    experimental_generator = sorted(
+        name for name in experimental_no_cover if name in generators
+    )
+    num_common = len(common_covered) + len(common_no_cover) - len(common_generator)
+    num_experimental = (
+        len(experimental_covered)
+        + len(experimental_no_cover)
+        - len(experimental_generator)
+    )
+    f.write("# Node Test Coverage\n")
+    f.write("## Summary\n")
+    if num_common:
+        f.write(
+            f"Node tests have covered {len(common_covered)}/{num_common} "
+            f"({len(common_covered) / float(num_common) * 100:.2f}%, {len(common_generator)} "
+            f"generators excluded) common operators.\n\n"
+        )
+    else:
+        f.write("Node tests have covered 0/0 (N/A) common operators. \n\n")
+    if num_experimental:
+        f.write(
+            "Node tests have covered {}/{} ({:.2f}%, {} generators excluded) "  # noqa: UP032
+            "experimental operators.\n\n".format(
+                len(experimental_covered),
+                num_experimental,
+                (len(experimental_covered) / float(num_experimental) * 100),
+                len(experimental_generator),
+            )
+        )
+    else:
+        f.write("Node tests have covered 0/0 (N/A) experimental operators.\n\n")
+    titles = [
+        "💚Covered Common Operators",
+        "💔No Cover Common Operators",
+        "💚Covered Experimental Operators",
+        "💔No Cover Experimental Operators",
+    ]
+    all_lists = [
+        common_covered,
+        common_no_cover,
+        experimental_covered,
+        experimental_no_cover,
+    ]
+    for t in titles:
+        f.write(f"* [{t[9:]}](#{t[9:].lower().replace(' ', '-')})\n")
+    f.write("\n")
+    for t, l in zip(titles, all_lists):  # noqa: E741
+        f.write(f"## {t}\n")
+        for s in l:
+            f.write(f"### {s}")
+            if s in node_tests:
+                f.write(
+                    f"\nThere are {len(node_tests[s])} test cases, listed as following:\n"
+                )
+                for summary, code in sorted(node_tests[s]):
+                    f.write("
\n")
+                    f.write(f"{summary}\n\n")
+                    f.write(f"```python\n{code}\n```\n\n")
+                    f.write("
\n")
+            else:  # noqa: PLR5501
+                if s in generators:
+                    f.write(" (random generator operator)\n")
+                else:
+                    f.write(" (call for test cases)\n")
+            f.write("\n\n")
+        f.write("
\n\n")
+
+
+def gen_model_test_coverage(
+    schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
+) -> None:
+    f.write("# Model Test Coverage\n")
+    # Process schemas
+    schema_dict = {}
+    for schema in schemas:
+        schema_dict[schema.name] = schema
+    # Load models from each model test using Runner.prepare_model_data
+    # Need to grab associated nodes
+    attrs: dict[str, dict[str, list[Any]]] = {}
+    model_paths: list[Any] = []
+    for rt in load_model_tests(kind="real"):
+        if rt.url.startswith("onnx/backend/test/data/light/"):
+            # testing local files
+            model_name = os.path.normpath(
+                os.path.join(os.path.dirname(__file__), "..", "..", "..", rt.url)
+            )
+            if not os.path.exists(model_name):
+                raise FileNotFoundError(f"Unable to find model {model_name!r}.")
+            model_paths.append(model_name)
+        else:
+            model_dir = Runner.prepare_model_data(rt)
+            model_paths.append(os.path.join(model_dir, "model.onnx"))
+    model_paths.sort()
+    model_written = False
+    for model_pb_path in model_paths:
+        model = load(model_pb_path)
+        if ml:
+            ml_present = False
+            for opset in model.opset_import:
+                if opset.domain == "ai.onnx.ml":
+                    ml_present = True
+            if not ml_present:
+                continue
+            else:
+                model_written = True
+        f.write(f"## {model.graph.name}\n")
+        # Deconstruct model
+        num_covered = 0
+        for node in model.graph.node:
+            if node.op_type in common_covered or node.op_type in experimental_covered:
+                num_covered += 1
+                # Add details of which nodes are/aren't covered
+                # Iterate through and store each node's attributes
+                for attr in node.attribute:
+                    if node.op_type not in attrs:
+                        attrs[node.op_type] = {}
+                    if attr.name not in attrs[node.op_type]:
+                        attrs[node.op_type][attr.name] = []
+                    if attr.type == AttributeProto.FLOAT:
+                        if attr.f not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.f)
+                    elif attr.type == AttributeProto.INT:
+                        if attr.i not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.i)
+                    elif attr.type == AttributeProto.STRING:
+                        if attr.s not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.s)
+                    elif attr.type == AttributeProto.TENSOR:
+                        if attr.t not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.t)
+                    elif attr.type == AttributeProto.GRAPH:
+                        if attr.g not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.g)
+                    elif attr.type == AttributeProto.FLOATS:
+                        if attr.floats not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.floats)
+                    elif attr.type == AttributeProto.INTS:
+                        if attr.ints not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.ints)
+                    elif attr.type == AttributeProto.STRINGS:
+                        if attr.strings not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.strings)
+                    elif attr.type == AttributeProto.TENSORS:
+                        if attr.tensors not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.tensors)
+                    elif attr.type == AttributeProto.GRAPHS:
+                        if attr.graphs not in attrs[node.op_type][attr.name]:
+                            attrs[node.op_type][attr.name].append(attr.graphs)
+        f.write(
+            f"\n{model.graph.name} has {num_covered} nodes. "
+            f"Of these, {len(model.graph.node)} are covered by node tests "
+            f"({100.0 * float(num_covered) / float(len(model.graph.node))}%)\n\n\n"
+        )
+        # Iterate through attrs, print
+        f.write("
\n")
+        f.write("nodes\n\n")
+        for op in sorted(attrs):
+            f.write("
\n")
+            # Get total number of attributes for node schema
+            f.write(
+                f"{op}: {len(attrs[op])} out of {len(schema_dict[op].attributes)} attributes covered\n\n"
+            )
+            for attribute in sorted(schema_dict[op].attributes):
+                if attribute in attrs[op]:
+                    f.write(f"{attribute}: {len(attrs[op][attribute])}\n")
+                else:
+                    f.write(f"{attribute}: 0\n")
+            f.write("
\n")
+        f.write("
\n\n\n")
+    if not model_written and ml:
+        f.write("No model tests present for selected domain\n")
+
+
+def gen_overall_test_coverage(
+    f: IO[Any],
+) -> None:
+    f.write("# Overall Test Coverage\n")
+    f.write("## To be filled.\n")
+
+
+def gen_spdx(f: IO[Any]) -> None:
+    f.write("\n")
+
+
+def main() -> None:
+    base_dir = os.path.dirname(
+        os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+    )
+    docs_dir = os.path.join(base_dir, "docs")
+    schemas = defs.get_all_schemas()
+
+    has_ml = is_ml(schemas)
+    fname = os.path.join(docs_dir, "TestCoverage.md")
+    with open(fname, "w+", newline="", encoding="utf-8") as f:  # type: ignore
+        gen_spdx(f)
+        gen_outlines(f, False)
+        gen_node_test_coverage(schemas, f, False)
+        gen_model_test_coverage(schemas, f, False)
+        gen_overall_test_coverage(f)
+
+    if has_ml:
+        fname = os.path.join(docs_dir, "TestCoverage-ml.md")
+        with open(fname, "w+", newline="", encoding="utf-8") as f:  # type: ignore
+            gen_spdx(f)
+            gen_outlines(f, True)
+            gen_node_test_coverage(schemas, f, True)
+            gen_model_test_coverage(schemas, f, True)
+            gen_overall_test_coverage(f)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/checker.cc b/pythonProject/.venv/Lib/site-packages/onnx/checker.cc
new file mode 100644
index 0000000000000000000000000000000000000000..80ec57f39c07ea16d41353ae7808afd53553b12c
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/checker.cc
@@ -0,0 +1,1057 @@
+// Copyright (c) ONNX Project Contributors
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#include "onnx/checker.h"
+
+#include 
+#include 
+#include 
+#include 
+
+#include "onnx/common/file_utils.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/tensor_proto_util.h"
+#include "onnx/shape_inference/implementation.h"
+#include "onnx/string_utils.h"
+
+#ifdef _WIN32
+#include "onnx/common/path.h"
+#endif
+
+namespace ONNX_NAMESPACE {
+namespace checker {
+
+#define enforce_has_field(proto, field)                                              \
+  do {                                                                               \
+    if (!proto.has_##field()) {                                                      \
+      fail_check("Field '", #field, "' of '", #proto, "' is required but missing."); \
+    }                                                                                \
+  } while (0)
+
+#define enforce_non_empty_field(proto, field)                                            \
+  do {                                                                                   \
+    if (proto.field().empty()) {                                                         \
+      fail_check("Field '", #field, "' of '", #proto, "' is required to be non-empty."); \
+    }                                                                                    \
+  } while (0)
+
+void check_value_info(const ValueInfoProto& value_info, const CheckerContext& ctx) {
+  enforce_non_empty_field(value_info, name);
+  // Relax constraint for subgraph input/output.
+  if (!ctx.is_main_graph())
+    return;
+  enforce_has_field(value_info, type);
+  const auto value_case = value_info.type().value_case();
+  switch (value_case) {
+    case TypeProto::kTensorType: {
+      const auto& type = value_info.type().tensor_type();
+      enforce_has_field(type, elem_type);
+      enforce_has_field(type, shape);
+    } break;
+    case TypeProto::kOptionalType: {
+      const auto& type = value_info.type().optional_type();
+      enforce_has_field(type, elem_type);
+    } break;
+    case TypeProto::kSequenceType: {
+      const auto& type = value_info.type().sequence_type();
+      enforce_has_field(type, elem_type);
+    } break;
+    case TypeProto::kMapType: {
+      const auto& type = value_info.type().map_type();
+      enforce_has_field(type, key_type);
+      enforce_has_field(type, value_type);
+    } break;
+#ifdef ONNX_ML
+    case TypeProto::kOpaqueType:
+      break;
+#endif
+    case TypeProto::kSparseTensorType: {
+      const auto& type = value_info.type().sparse_tensor_type();
+      enforce_has_field(type, elem_type);
+      enforce_has_field(type, shape);
+    } break;
+
+    default:
+      fail_check("Unrecognized type value case (value_info name: ", value_info.name(), "): ", value_case);
+  }
+}
+
+void check_tensor(const TensorProto& tensor, const CheckerContext& ctx) {
+  enforce_has_field(tensor, data_type);
+  if (tensor.data_type() == TensorProto::UNDEFINED) {
+    fail_check("setting data_type field (tensor name: ", tensor.name(), ") to UNDEFINED is not allowed");
+  }
+
+  int num_value_fields = 0;
+
+  const char* value_field = nullptr;
+
+#define check_data_field(field)               \
+  bool has_##field = !tensor.field().empty(); \
+  if (has_##field) {                          \
+    ++num_value_fields;                       \
+    value_field = #field;                     \
+  }
+
+  check_data_field(float_data);
+  check_data_field(int32_data);
+  check_data_field(string_data);
+  check_data_field(int64_data);
+  check_data_field(raw_data);
+  check_data_field(double_data);
+  check_data_field(uint64_data);
+
+#undef check_data_field
+
+  bool stored_externally = tensor.has_data_location() && tensor.data_location() == TensorProto::EXTERNAL;
+  if (stored_externally) {
+    if (num_value_fields != 0) {
+      fail_check(
+          "Data of TensorProto ( tensor name: ",
+          tensor.name(),
+          ") is stored externally and should not have data field.",
+          value_field);
+    }
+
+    bool has_location = false;
+    for (const StringStringEntryProto& entry : tensor.external_data()) {
+      if (entry.has_key() && entry.has_value() && entry.key() == "location") {
+        has_location = true;
+        resolve_external_data_location(ctx.get_model_dir(), entry.value(), tensor.name());
+      }
+    }
+    if (!has_location) {
+      fail_check("TensorProto ( tensor name: ", tensor.name(), ") is stored externally but doesn't have a location.");
+    }
+    return;
+  }
+  int64_t nelem = 1;
+  for (auto x : tensor.dims()) {
+    nelem *= x;
+  }
+  if (nelem == 0 && num_value_fields != 0) {
+    fail_check("TensorProto (tensor name: ", tensor.name(), ") is 0-element but contains data!");
+  }
+  if (nelem != 0 && num_value_fields != 1) {
+    fail_check("TensorProto (tensor name: ", tensor.name(), ") should contain one and only one value field.");
+  }
+  if (has_raw_data) {
+    if (tensor.data_type() == TensorProto::STRING) {
+      fail_check("STRING data (tensor name: ", tensor.name(), ") should not be stored in raw_data field");
+    }
+    return;
+  } else {
+#define check_field(field)               \
+  if (nelem != 0 && !has_##field) {      \
+    fail_check(                          \
+        "values of data_type '",         \
+        tensor.data_type(),              \
+        "' should be stored in field '", \
+        #field,                          \
+        "' instead of '",                \
+        value_field,                     \
+        "'");                            \
+  }
+
+    switch (tensor.data_type()) {
+      case TensorProto::FLOAT:
+      case TensorProto::COMPLEX64:
+        check_field(float_data);
+        break;
+
+      case TensorProto::DOUBLE:
+      case TensorProto::COMPLEX128:
+        check_field(double_data);
+        break;
+
+      case TensorProto::INT32:
+      case TensorProto::UINT8:
+      case TensorProto::INT8:
+      case TensorProto::UINT16:
+      case TensorProto::INT16:
+      case TensorProto::BOOL:
+      case TensorProto::FLOAT16:
+      case TensorProto::BFLOAT16:
+      case TensorProto::FLOAT8E4M3FN:
+      case TensorProto::FLOAT8E4M3FNUZ:
+      case TensorProto::FLOAT8E5M2:
+      case TensorProto::FLOAT8E5M2FNUZ:
+      case TensorProto::FLOAT8E8M0:
+      case TensorProto::UINT4:
+      case TensorProto::INT4:
+      case TensorProto::FLOAT4E2M1:
+        check_field(int32_data);
+        break;
+
+      case TensorProto::INT64:
+        check_field(int64_data);
+        break;
+
+      case TensorProto::UINT32:
+      case TensorProto::UINT64:
+        check_field(uint64_data);
+        break;
+
+      case TensorProto::STRING:
+        check_field(string_data);
+        break;
+
+      default:
+        fail_check("Unrecognized data_type (tensor name: ", tensor.name(), "): ", tensor.data_type());
+    }
+  }
+
+#undef check_field
+}
+
+void check_sequence(const SequenceProto& sequence, const CheckerContext& ctx) {
+  enforce_has_field(sequence, elem_type);
+  if (sequence.elem_type() == SequenceProto::TENSOR) {
+    for (const TensorProto& tensor : sequence.tensor_values()) {
+      check_tensor(tensor, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::SPARSE_TENSOR) {
+    for (const SparseTensorProto& sparse_tensor : sequence.sparse_tensor_values()) {
+      check_sparse_tensor(sparse_tensor, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::SEQUENCE) {
+    for (const SequenceProto& seq : sequence.sequence_values()) {
+      check_sequence(seq, ctx);
+    }
+  } else if (sequence.elem_type() == SequenceProto::MAP) {
+    for (const MapProto& map : sequence.map_values()) {
+      check_map(map, ctx);
+    }
+  } else {
+    fail_check(
+        "Sequence ( Structure name: ",
+        sequence.name(),
+        ", elem_type: ",
+        sequence.elem_type(),
+        ") is not have a valid element type.");
+  }
+}
+
+void check_optional(const OptionalProto& optional, const CheckerContext& ctx) {
+  enforce_has_field(optional, elem_type);
+  if (optional.elem_type() == OptionalProto::UNDEFINED) {
+    return;
+  } else if (optional.elem_type() == OptionalProto::TENSOR) {
+    if (optional.has_tensor_value())
+      check_tensor(optional.tensor_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::SPARSE_TENSOR) {
+    if (optional.has_sparse_tensor_value())
+      check_sparse_tensor(optional.sparse_tensor_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::SEQUENCE) {
+    if (optional.has_sequence_value())
+      check_sequence(optional.sequence_value(), ctx);
+  } else if (optional.elem_type() == OptionalProto::MAP) {
+    if (optional.has_map_value())
+      check_map(optional.map_value(), ctx);
+  } else {
+    fail_check(
+        "Optional ( Structure name: ",
+        optional.name(),
+        ", elem_type: ",
+        optional.elem_type(),
+        ") is not have a valid element type.");
+  }
+}
+
+void check_map(const MapProto& map, const CheckerContext& ctx) {
+  enforce_has_field(map, key_type);
+  if (map.key_type() == TensorProto::UNDEFINED) {
+    fail_check("setting key_type field (map name: ", map.name(), ") to UNDEFINED is not allowed");
+  }
+  // Check if key is a valid type, specifically INT8, INT16, INT32, INT64,
+  // UINT8, UINT16, UINT32, UINT64, or STRING.
+  if ((map.key_type() == TensorProto::FLOAT) || (map.key_type() == TensorProto::BOOL) ||
+      (map.key_type() == TensorProto::FLOAT16) || (map.key_type() == TensorProto::COMPLEX64) ||
+      (map.key_type() == TensorProto::COMPLEX128)) {
+    fail_check(
+        "setting key_type field (map name: ",
+        map.name(),
+        ") to invalid TensorProto key_type ",
+        map.key_type(),
+        " is not allowed");
+  }
+
+  // MapProto will use either keys or string_keys, so only one should be > 0.
+  if ((map.keys_size() > 0) && (map.string_keys_size() > 0)) {
+    fail_check("Map (name: ", map.name(), ") should not contain more than one keys field.");
+  }
+
+  int num_keys = map.keys_size() + map.string_keys_size();
+  int num_values = 0;
+
+  enforce_has_field(map, values);
+  check_sequence(map.values(), ctx);
+
+  if (map.values().elem_type() == SequenceProto::TENSOR) {
+    num_values = map.values().tensor_values_size();
+  } else if (map.values().elem_type() == SequenceProto::SPARSE_TENSOR) {
+    num_values = map.values().sparse_tensor_values_size();
+  } else if (map.values().elem_type() == SequenceProto::SEQUENCE) {
+    num_values = map.values().sequence_values_size();
+  } else if (map.values().elem_type() == SequenceProto::MAP) {
+    num_values = map.values().map_values_size();
+  }
+
+  if (num_keys != num_values) {
+    fail_check("Length of map keys and map values are not the same (map name: ", map.name(), ")");
+  }
+}
+
+// Check that the index data stored in a SparseTensorProto is valid.
+// indices: a 1-dimensional tensor; indices[i] represents the
+// linearized index value for the i-th nonzero value.
+static void
+check_sparse_tensor_indices_1(const TensorProto& indices, const SparseTensorProto& sparse_tensor_proto, size_t nnz) {
+  int dense_rank = sparse_tensor_proto.dims_size();
+  int64_t dense_size = 1;
+  for (int i = 0; i < dense_rank; ++i)
+    dense_size *= sparse_tensor_proto.dims(i);
+  if (static_cast(indices.dims(0)) != nnz) {
+    fail_check("Sparse tensor indices (", indices.name(), ") has ", indices.dims(0), " values, but NNZ is ", nnz);
+  }
+
+  // Check if indices appear in ascending order, and if they have valid
+  // values. The i-th value in index_data is the linear index of the i-th
+  // non-zero value.
+  const std::vector index_data = ParseData(&indices);
+
+  int64_t prev_index = -1;
+  for (size_t i = 0; i < nnz; ++i) {
+    int64_t curr_index = index_data[i]; // linearized index of i-th value
+    if (curr_index < 0 || curr_index >= dense_size) {
+      fail_check(
+          "Sparse tensor (",
+          indices.name(),
+          ") index value at position [",
+          i,
+          "] out of range [0, ",
+          dense_size - 1,
+          "]");
+    }
+    if (curr_index <= prev_index) {
+      fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, "] not in sorted order.");
+    }
+    prev_index = curr_index;
+  }
+}
+
+// Check that the index data stored in a SparseTensorProto is valid.
+// indices: a 2-dimensional tensor; indices[i,j] represents the j-th
+// index value for the i-th nonzero value.
+static void
+check_sparse_tensor_indices_2(const TensorProto& indices, const SparseTensorProto& sparse_tensor_proto, size_t nnz) {
+  int dense_rank = sparse_tensor_proto.dims_size();
+  if (static_cast(indices.dims(0)) != nnz) {
+    fail_check("Sparse tensor indices (", indices.name(), ") first dimension size does not equal NNZ.");
+  }
+  if (indices.dims(1) != dense_rank) {
+    fail_check("Sparse tensor indices (", indices.name(), ") second dimension size does not match rank of tensor.");
+  }
+
+  // Check if indices appear in ascending order, and if they have valid
+  // values.
+  const std::vector index_data = ParseData(&indices);
+  int64_t prev_index = -1;
+  for (size_t i = 0; i < nnz; ++i) {
+    int64_t curr_index = 0; // linearized index of i-th value
+    for (int j = 0; j < dense_rank; ++j) {
+      auto index_ij = index_data[i * dense_rank + j];
+      if ((index_ij < 0) || (index_ij >= sparse_tensor_proto.dims(j))) {
+        fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, ",", j, "] out of range.");
+      }
+      curr_index = curr_index * sparse_tensor_proto.dims(j) + index_ij;
+    }
+    if (curr_index <= prev_index) {
+      fail_check(
+          "Sparse tensor (", indices.name(), ") index value at position [", i, "] not in lexicographic sorted order.");
+    }
+    prev_index = curr_index;
+  }
+}
+
+void check_sparse_tensor(const SparseTensorProto& sparse_tensor_proto, const CheckerContext& ctx) {
+  enforce_has_field(sparse_tensor_proto, values);
+
+  const TensorProto& values = sparse_tensor_proto.values();
+  check_tensor(values, ctx);
+
+  // values must be a tensor of shape [NNZ]
+  // Currently we restrict the value associated with a particular index-tuple
+  // to be a single value. In the future, if there is a requirement,
+  // we may extend this to permit the value to be a "sub-tensor", in which
+  // case values will have dimension > 1.
+  if (values.dims_size() != 1) {
+    fail_check("Sparse tensor values (", values.name(), ") must have rank 1.");
+  }
+  size_t nnz = static_cast(values.dims(0));
+  int dense_rank = sparse_tensor_proto.dims_size();
+  if (dense_rank == 0) {
+    fail_check("Sparse tensor (", values.name(), ") must have a dense-rank > 0");
+  }
+  for (int i = 0; i < dense_rank; ++i) {
+    if (sparse_tensor_proto.dims(i) <= 0) {
+      fail_check("Sparse tensor (", values.name(), ") dimensions are not positive.");
+    }
+  }
+
+  if (sparse_tensor_proto.has_indices()) {
+    const TensorProto& indices = sparse_tensor_proto.indices();
+    check_tensor(indices, ctx);
+    if (indices.data_type() != TensorProto::INT64) {
+      fail_check("Sparse tensor indices (", indices.name(), ") must have INT64 type.");
+    }
+    switch (indices.dims().size()) {
+      case 1:
+        // Indices in linearized format
+        check_sparse_tensor_indices_1(indices, sparse_tensor_proto, nnz);
+        return;
+      case 2:
+        // Check COO-style index. E.g., an index for a 3D tensor is a 3-tuple.
+        check_sparse_tensor_indices_2(indices, sparse_tensor_proto, nnz);
+        return;
+      default:
+        fail_check("Sparse tensor indices (", indices.name(), ") must have rank 1 or 2.");
+    }
+  } else if (nnz != 0) {
+    fail_check("Sparse tensor (", values.name(), ") has no index values.");
+  }
+}
+
+// NB: This is a generic "attribute well-formedness" check, it doesn't
+// actually test if an attribute is valid per a schema
+void check_attribute(const AttributeProto& attr, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
+  enforce_non_empty_field(attr, name);
+
+  if (ctx.get_ir_version() >= 0x00000002) {
+    enforce_has_field(attr, type);
+  }
+
+  int used_fields = 0;
+
+#define check_type(expected_type)                                                     \
+  if (attr.has_type() && attr.type() != expected_type) {                              \
+    fail_check("type field and data field mismatch in attribute ", attr.name(), "."); \
+  }
+
+#define check_singular_field(field, type) \
+  if (attr.has_##field()) {               \
+    ++used_fields;                        \
+    check_type(type);                     \
+  }
+
+#define check_repeated_field(field, type) \
+  if (attr.field##_size() > 0) {          \
+    ++used_fields;                        \
+    check_type(type);                     \
+  }
+
+  check_singular_field(f, AttributeProto::FLOAT);
+  check_singular_field(i, AttributeProto::INT);
+  check_singular_field(s, AttributeProto::STRING);
+  check_singular_field(t, AttributeProto::TENSOR);
+  check_singular_field(g, AttributeProto::GRAPH);
+  check_singular_field(tp, AttributeProto::TYPE_PROTO);
+  check_singular_field(sparse_tensor, AttributeProto::SPARSE_TENSOR);
+  check_repeated_field(floats, AttributeProto::FLOATS);
+  check_repeated_field(ints, AttributeProto::INTS);
+  check_repeated_field(strings, AttributeProto::STRINGS);
+  check_repeated_field(tensors, AttributeProto::TENSORS);
+  check_repeated_field(graphs, AttributeProto::GRAPHS);
+  check_repeated_field(sparse_tensors, AttributeProto::SPARSE_TENSORS);
+  check_repeated_field(type_protos, AttributeProto::TYPE_PROTOS);
+
+#undef check_type
+#undef check_singular_field
+#undef check_repeated_field
+
+  // Normally, used_fields is expected to be 1.
+  // In proto3, when the value to be set is type default value (say 0 for
+  // int), used_fields may be 0.
+  if (used_fields > 1) {
+    fail_check("Attribute (name: ", attr.name(), ") should not contain more than one value field.");
+  }
+
+  if (!ctx.is_main_graph()) {
+    // It's an attribute of a node in function body.
+    if (attr.has_ref_attr_name() && used_fields != 0) {
+      // The attribute proto is supposed to refer to data outside and does not
+      // have its own value field set.
+      fail_check("Attribute (name: ", attr.name(), ") should refer to attribute in parent node.");
+    }
+  }
+
+  if (attr.has_t()) {
+    check_tensor(attr.t(), ctx);
+  }
+
+  if (attr.has_sparse_tensor()) {
+    check_sparse_tensor(attr.sparse_tensor(), ctx);
+  }
+
+  if (attr.has_g()) {
+    CheckerContext subgraph_ctx(ctx);
+    subgraph_ctx.set_is_main_graph(false);
+    check_graph(attr.g(), subgraph_ctx, lex_ctx);
+  }
+
+  for (const auto& tensor : attr.tensors()) {
+    check_tensor(tensor, ctx);
+  }
+  for (const auto& sparse_tensor : attr.sparse_tensors()) {
+    check_sparse_tensor(sparse_tensor, ctx);
+  }
+  if (!attr.graphs().empty()) {
+    CheckerContext subgraph_ctx(ctx);
+    subgraph_ctx.set_is_main_graph(false);
+    for (const auto& graph : attr.graphs()) {
+      check_graph(graph, subgraph_ctx, lex_ctx);
+    }
+  }
+}
+
+static void print_warning_if_has_experimental(const std::unordered_set& used_experimental_ops) {
+  if (!used_experimental_ops.empty()) {
+    std::string all_experimental_ops;
+    for (const auto& op : used_experimental_ops) {
+      all_experimental_ops += " " + op + ",";
+    }
+    // Remove the last comma which is unnecessary
+    all_experimental_ops.pop_back();
+    std::cout << "Warning: Model contains experimental ops:" + all_experimental_ops << '\n';
+  }
+}
+
+void check_node(const NodeProto& node, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
+  enforce_non_empty_field(node, op_type);
+
+  if (node.input().empty() && node.output().empty()) {
+    fail_check("NodeProto (name: ", node.name(), ", type: ", node.op_type(), ") has zero input and zero output.");
+  }
+
+  // Resolve domain for node
+  const auto& opset_imports = ctx.get_opset_imports();
+  auto dit = opset_imports.find(node.domain());
+  if (dit == opset_imports.end()) {
+    fail_check("No opset import for domain '" + node.domain() + "'");
+  }
+  auto domain_version = dit->second;
+
+  // for ops referencing local functions, there is no schema to verify it.
+  // will add a check to verify consistency between these ops and local functions.
+  std::unordered_set seen_attr_names{};
+  for (const auto& attr : node.attribute()) {
+    if (!seen_attr_names.insert(attr.name()).second) {
+      fail_check("Attribute '", attr.name(), "' appeared multiple times.");
+    };
+
+    check_attribute(attr, ctx, lex_ctx);
+  }
+
+  // This issue will be caught by check_graph instead
+  if (check_is_experimental_op(node)) {
+    return;
+  }
+
+  const auto* schema = ctx.get_schema_registry()->GetSchema(node.op_type(), domain_version, node.domain());
+  if (!schema) {
+    if (node.domain() == ONNX_DOMAIN || node.domain() == AI_ONNX_ML_DOMAIN || node.domain() == "ai.onnx" ||
+        node.domain() == AI_ONNX_TRAINING_DOMAIN || ctx.check_custom_domain()) {
+      // fail the checker if op is in built-in domains or if it has no schema when `check_custom_domain` is true
+      fail_check(
+          "No Op registered for " + node.op_type() + " with domain_version of " +
+          ONNX_NAMESPACE::to_string(domain_version));
+    }
+  } else if (schema->Deprecated()) {
+    fail_check(
+        "Op registered for " + node.op_type() + " is deprecated in domain_version of " +
+        ONNX_NAMESPACE::to_string(domain_version));
+  } else {
+    schema->Verify(node);
+  }
+}
+
+void check_graph(const GraphProto& graph, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
+  enforce_non_empty_field(graph, name);
+
+  for (const auto& value_info : graph.input()) {
+    check_value_info(value_info, ctx);
+  }
+  for (const auto& value_info : graph.output()) {
+    check_value_info(value_info, ctx);
+  }
+
+  // Inherit values available in outer scope
+  // Note that we do not allow shadowing, so the presence of an already-defined
+  // name is always an error.
+  LexicalScopeContext lex_ctx{parent_lex};
+
+  for (const auto& value_info : graph.input()) {
+    // TODO: If shadowing isn't allowed, this should maybe use
+    // this_or_ancestor_graph_has
+    if (lex_ctx.this_graph_has(value_info.name())) {
+      fail_check(
+          "Graph must be in single static assignment (SSA) form, however '",
+          value_info.name(),
+          "' has been used as graph input names multiple times.");
+    }
+    lex_ctx.add(value_info.name());
+  }
+
+  std::unordered_set initializer_name_checker;
+
+  for (const auto& init : graph.initializer()) {
+    enforce_has_field(init, name);
+    const auto& name = init.name();
+    if (name.empty()) {
+      fail_check("Tensor initializers must have a non-empty name");
+    }
+
+    if (!initializer_name_checker.emplace(name).second) {
+      fail_check(name + " initializer name is not unique");
+    }
+
+    check_tensor(init, ctx);
+
+    if (ctx.get_ir_version() <= 0x00000003) {
+      // Initializers are a subset of graph inputs for IR_VERSION <= 3
+      if (!lex_ctx.this_graph_has(name)) {
+        fail_check(name + " in initializer but not in graph input");
+      }
+    } else {
+      // An initializer is allowed to have the same name as an input,
+      // but is not required to (for IR_VERSION >= 4)
+      lex_ctx.add(name);
+    }
+  }
+
+  for (const auto& sparse_init : graph.sparse_initializer()) {
+    const auto& values = sparse_init.values();
+    enforce_has_field(values, name);
+    const auto& name = values.name();
+    if (name.empty()) {
+      fail_check("Sparse tensor initializers must have a non-empty name");
+    }
+    if (!initializer_name_checker.insert(name).second) {
+      fail_check(name + " sparse initializer name is not unique across initializers and sparse_initializers");
+    }
+    check_sparse_tensor(sparse_init, ctx);
+    lex_ctx.add(name);
+  }
+  std::unordered_set used_experimental_ops;
+  for (const auto& node : graph.node()) {
+    // nodes must be in topologically sorted order
+    for (const auto& input : node.input()) {
+      // explicit optional input
+      if (input.empty()) {
+        continue;
+      }
+      if (!lex_ctx.this_or_ancestor_graph_has(input)) {
+        fail_check(
+            "Nodes in a graph must be topologically sorted, however input '",
+            input,
+            "' of node: \n",
+            "name: ",
+            node.name(),
+            " OpType: ",
+            node.op_type(),
+            "\n is not output of any previous nodes.");
+      }
+    }
+
+    if (check_is_experimental_op(node)) {
+      used_experimental_ops.insert(node.op_type());
+    }
+
+    // This needs to happen before SSA check since we don't want to recurse and
+    // find that outputs from control flow ops are colliding with names in the
+    // inner block
+
+    ONNX_TRY {
+      check_node(node, ctx, lex_ctx);
+    }
+    ONNX_CATCH(ValidationError & ex) {
+      ONNX_HANDLE_EXCEPTION([&]() {
+        ex.AppendContext("Bad node spec for node. Name: " + node.name() + " OpType: " + node.op_type());
+        ONNX_THROW_EX(ex);
+      });
+    }
+    // check for SSA form
+    for (const auto& output : node.output()) {
+      // optional output
+      if (output.empty()) {
+        continue;
+      }
+
+      if (lex_ctx.this_or_ancestor_graph_has(output)) {
+        fail_check(
+            "Graph must be in single static assignment (SSA) form, however '",
+            output,
+            "' has been used as output names multiple times.");
+      }
+      lex_ctx.add(output);
+    }
+  }
+  for (const auto& value_info : graph.output()) {
+    if (!lex_ctx.this_graph_has(value_info.name())) {
+      fail_check("Graph output '", value_info.name(), "' is not an output of any node in graph.");
+    }
+  }
+
+  print_warning_if_has_experimental(used_experimental_ops);
+}
+
+// Utilify function to get the imported version of domain from opset imports
+// Returns -1 if requested domain is not found in the opset_imports
+static int get_version_for_domain(
+    const std::string& domain,
+    const std::unordered_map& opset_imports) {
+  auto it = opset_imports.find(domain);
+  if (it == opset_imports.end()) {
+    return -1;
+  }
+
+  return it->second;
+}
+
+void check_opset_compatibility(
+    const NodeProto& node,
+    const CheckerContext& ctx,
+    const std::unordered_map& func_opset_imports,
+    const std::unordered_map& model_opset_imports) {
+  auto func_opset_version = get_version_for_domain(node.domain(), func_opset_imports);
+  auto model_opset_version = get_version_for_domain(node.domain(), model_opset_imports);
+
+  if (func_opset_version == -1) {
+    fail_check("No Opset registered for domain " + node.domain());
+  }
+
+  if (model_opset_version == -1) {
+    // model does not include opset import for a node present in function body.
+    // This is ok as along as the opset import is present in function level opset imports.
+    return;
+  }
+
+  if (func_opset_version == model_opset_version) {
+    // both versions are same, no need to verify schema.
+    return;
+  }
+
+  const auto* schema_for_model_import =
+      ctx.get_schema_registry()->GetSchema(node.op_type(), model_opset_version, node.domain());
+
+  const auto* schema_for_function_import =
+      ctx.get_schema_registry()->GetSchema(node.op_type(), func_opset_version, node.domain());
+
+  if (!schema_for_model_import && !schema_for_function_import) {
+    // the op belongs to a custom domain so we cannot verify schema
+    return;
+  }
+
+  // if schema is present for 1 but not other or the schema since versions do not match then raise an error
+  if (!schema_for_model_import || !schema_for_function_import ||
+      schema_for_function_import->since_version() != schema_for_model_import->since_version()) {
+    fail_check(
+        "Opset import for domain " + node.domain() + " in function op " + node.op_type() +
+        "is not compatible with the version imported by model. FunctionOp imports version " +
+        ONNX_NAMESPACE::to_string(func_opset_version) + " whereas model imports version " +
+        ONNX_NAMESPACE::to_string(model_opset_version));
+  }
+}
+
+void check_model_local_functions(
+    const ModelProto& model,
+    const CheckerContext& ctx,
+    const LexicalScopeContext& parent_lex) {
+  // make a copy of model opset imports to maintain a main copy of opset imports across the model and
+  // all model local functions to verify opset compatibility
+  std::unordered_map model_opset_imports(ctx.get_opset_imports());
+
+  // merge the opset imports from every function in model_opset_imports
+  // only add the opset import if an entry for it does not exist in model_opset_imports
+  // if there is an entry then the compatibility will be checked later on in check_opset_compatibility
+  // called by check_function.
+  for (const auto& function_proto : model.functions()) {
+    for (const auto& opset_import : function_proto.opset_import()) {
+      if (get_version_for_domain(opset_import.domain(), model_opset_imports) == -1) {
+        model_opset_imports[opset_import.domain()] = opset_import.version();
+      }
+    }
+  }
+
+  CheckerContext ctx_copy = ctx;
+  ctx_copy.set_opset_imports(model_opset_imports);
+
+  for (const auto& function_proto : model.functions()) {
+    check_function(function_proto, ctx_copy, parent_lex);
+  }
+}
+
+void check_function(const FunctionProto& function, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
+  enforce_non_empty_field(function, name);
+
+  if (ctx.get_ir_version() >= 0x00000008) {
+    enforce_has_field(function, domain);
+  }
+
+  const auto& model_opset_imports = ctx.get_opset_imports();
+  CheckerContext ctx_copy = ctx;
+
+  std::unordered_map func_opset_imports;
+  for (auto& relied_opset : function.opset_import()) {
+    func_opset_imports[relied_opset.domain()] = static_cast(relied_opset.version());
+  }
+
+  ctx_copy.set_opset_imports(func_opset_imports);
+
+  LexicalScopeContext lex_ctx{parent_lex};
+
+  for (const auto& input : function.input()) {
+    // TODO: If shadowing isn't allowed, this should maybe use
+    // this_or_ancestor_graph_has
+    if (lex_ctx.this_graph_has(input)) {
+      fail_check(
+          "Graph must be in single static assignment (SSA) form, however '", input, "' has been used multiple times.");
+    }
+    lex_ctx.add(input);
+  }
+
+  std::unordered_set outputs;
+  for (const auto& output : function.output()) {
+    auto result = outputs.insert(output);
+    if (!result.second) {
+      fail_check("function (", function.name(), ") should not have duplicate outputs specified.");
+    }
+  }
+
+  std::unordered_set attrs;
+  for (const auto& attr : function.attribute()) {
+    auto result = attrs.insert(attr);
+    if (!result.second) {
+      fail_check("function (", function.name(), ") should not have duplicate attributes specified.");
+    }
+  }
+  std::unordered_set used_experimental_ops;
+  for (const auto& node : function.node()) {
+    // nodes must be in topologically sorted order
+    for (const auto& input : node.input()) {
+      // explicit optional input
+      if (input.empty()) {
+        continue;
+      }
+      if (!lex_ctx.this_graph_has(input)) {
+        fail_check(
+            "Nodes in a function must be topologically sorted, however input '",
+            input,
+            "' of node: \n",
+            "Name: ",
+            node.name(),
+            " OpType: ",
+            node.op_type(),
+            "\n is neither output of any previous nodes nor input of the function.");
+      }
+    }
+
+    // check whether the opset version imported for a domain by function and model are
+    // compatible
+    if (!ctx_copy.skip_opset_compatibility_check())
+      check_opset_compatibility(node, ctx_copy, func_opset_imports, model_opset_imports);
+    if (check_is_experimental_op(node)) {
+      used_experimental_ops.insert(node.op_type());
+    }
+    check_node(node, ctx_copy, lex_ctx);
+
+    // check for SSA form
+    for (const auto& output : node.output()) {
+      // optional output
+      if (output.empty()) {
+        continue;
+      }
+      if (lex_ctx.this_or_ancestor_graph_has(output)) {
+        fail_check(
+            "Function must be in single static assignment (SSA) form, however '",
+            output,
+            "' has been used as output names multiple times.");
+      }
+      lex_ctx.add(output);
+    }
+  }
+  print_warning_if_has_experimental(used_experimental_ops);
+}
+
+static void check_model(const ModelProto& model, CheckerContext& ctx) {
+  if (!model.ir_version()) {
+    fail_check("The model does not have an ir_version set properly.");
+  }
+  if (model.ir_version() > IR_VERSION) {
+    fail_check("Your model ir_version ", model.ir_version(), " is higher than the checker's (", IR_VERSION, ").");
+  }
+  if (model.metadata_props_size() > 1) {
+    std::unordered_set keys;
+    for (const StringStringEntryProto& entry : model.metadata_props()) {
+      auto i = keys.insert(entry.key());
+      if (!i.second) {
+        fail_check("Your model has duplicate keys in metadata_props.");
+      }
+    }
+  }
+  ctx.set_ir_version(static_cast(model.ir_version()));
+  std::unordered_map opset_imports;
+  for (const auto& opset_import : model.opset_import()) {
+    opset_imports[opset_import.domain()] = static_cast(opset_import.version());
+  }
+  if (model.ir_version() >= 3) {
+    if (opset_imports.empty()) {
+      fail_check("model with IR version >= 3 must specify opset_import for ONNX");
+    }
+  } else {
+    if (opset_imports.empty())
+      opset_imports[ONNX_DOMAIN] = 1;
+    else {
+      fail_check("model with IR version < 3 cannot have opset_import specified");
+    }
+  }
+  ctx.set_opset_imports(opset_imports);
+  LexicalScopeContext lex_ctx;
+  check_graph(model.graph(), ctx, lex_ctx);
+
+  if (ctx.get_ir_version() >= 0x00000008) {
+    check_model_local_functions(model, ctx, lex_ctx);
+    // TODO: check consistency between local functions and ops referencing it.
+  }
+}
+
+void check_model(
+    const std::string& model_path,
+    bool full_check,
+    bool skip_opset_compatibility_check,
+    bool check_custom_domain) {
+  ModelProto model;
+  LoadProtoFromPath(model_path, model);
+
+  CheckerContext ctx;
+  std::string model_dir;
+  size_t pos = model_path.find_last_of("\\/");
+  if (pos != std::string::npos) {
+    model_dir = model_path.substr(0, pos + 1);
+  }
+  ctx.set_model_dir(model_dir);
+  ctx.set_skip_opset_compatibility_check(skip_opset_compatibility_check);
+  ctx.set_check_custom_domain(check_custom_domain);
+  check_model(model, ctx);
+
+  if (full_check) {
+    ShapeInferenceOptions options{true, 1, false};
+    ONNX_NAMESPACE::shape_inference::InferShapes(model, ctx.get_schema_registry(), options);
+  }
+}
+
+void check_model(
+    const ModelProto& model,
+    bool full_check,
+    bool skip_opset_compatibility_check,
+    bool check_custom_domain) {
+  CheckerContext ctx;
+  ctx.set_skip_opset_compatibility_check(skip_opset_compatibility_check);
+  ctx.set_check_custom_domain(check_custom_domain);
+  check_model(model, ctx);
+  if (full_check) {
+    ShapeInferenceOptions options{true, 1, false};
+    // Do not update the model in place by the check from shape inference
+    // because checker should not modify the original model
+    ModelProto copy = model;
+    ONNX_NAMESPACE::shape_inference::InferShapes(copy, ctx.get_schema_registry(), options);
+  }
+}
+
+std::string resolve_external_data_location(
+    const std::string& base_dir,
+    const std::string& location,
+    const std::string& tensor_name) {
+#ifdef _WIN32
+  std::filesystem::path base_dir_path(utf8str_to_wstring(base_dir));
+  std::filesystem::path file_path(utf8str_to_wstring(location));
+#else // POSIX
+  std::filesystem::path base_dir_path(base_dir);
+  std::filesystem::path file_path(location);
+#endif
+  if (file_path.empty()) {
+    fail_check("Location of external TensorProto ( tensor name: ", tensor_name, ") should not be empty.");
+  }
+  if (file_path.is_absolute()) {
+    fail_check(
+        "Location of external TensorProto ( tensor name: ",
+        tensor_name,
+        ") should be a relative path, but it is an absolute path: ",
+        location);
+  }
+  auto relative_path = file_path.lexically_normal().make_preferred();
+  // Check that normalized relative path doesn't contains ".."
+#ifdef _WIN32
+  if (relative_path.native().find(L"..", 0) != std::string::npos) {
+#else // POSIX
+  if (relative_path.native().find("..", 0) != std::string::npos) {
+#endif
+    fail_check(
+        "Data of TensorProto ( tensor name: ",
+        tensor_name,
+        ") should be file inside the ",
+        base_dir,
+        ", but the '",
+        location,
+        "' points outside the directory");
+  }
+  auto data_path = base_dir_path / relative_path;
+#ifdef _WIN32
+  auto data_path_str = wstring_to_utf8str(data_path.native());
+#else
+  auto data_path_str = data_path.native();
+#endif
+  // Check whether the file exists
+  if (data_path.empty() || (data_path_str[0] != '#' && !std::filesystem::exists(data_path))) {
+    fail_check(
+        "Data of TensorProto ( tensor name: ",
+        tensor_name,
+        ") should be stored in ",
+        data_path_str,
+        ", but it doesn't exist or is not accessible.");
+  }
+  // Do not allow symlinks or directories.
+  if (data_path.empty() || (data_path_str[0] != '#' && !std::filesystem::is_regular_file(data_path))) {
+    fail_check(
+        "Data of TensorProto ( tensor name: ",
+        tensor_name,
+        ") should be stored in ",
+        data_path_str,
+        ", but it is not regular file.");
+  }
+  return data_path_str;
+}
+
+static std::unordered_set experimental_ops = {
+    "ATen",
+    "Affine",
+    "ConstantFill",
+    "Crop",
+    "DynamicSlice",
+    "GRUUnit",
+    "GivenTensorFill",
+    "ImageScaler",
+    "ParametricSoftplus",
+    "Scale",
+    "ScaledTanh"};
+
+bool check_is_experimental_op(const NodeProto& node) {
+  return (node.domain() == ONNX_DOMAIN || node.domain() == "ai.onnx") && experimental_ops.count(node.op_type());
+}
+
+#undef enforce_has_field
+#undef enforce_non_empty_field
+
+} // namespace checker
+} // namespace ONNX_NAMESPACE
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/checker.h b/pythonProject/.venv/Lib/site-packages/onnx/checker.h
new file mode 100644
index 0000000000000000000000000000000000000000..e836f1e115389a03051c73edc15e6f17c140d12e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/checker.h
@@ -0,0 +1,190 @@
+// Copyright (c) ONNX Project Contributors
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include "onnx/defs/schema.h"
+#include "onnx/onnx-data.pb.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace checker {
+class ValidationError final : public std::runtime_error {
+ public:
+  using std::runtime_error::runtime_error;
+  const char* what() const noexcept override {
+    if (!expanded_message_.empty()) {
+      return expanded_message_.c_str();
+    }
+    return std::runtime_error::what();
+  }
+  void AppendContext(const std::string& context) {
+    expanded_message_ = ONNX_NAMESPACE::MakeString(std::runtime_error::what(), "\n\n==> Context: ", context);
+  }
+
+ private:
+  std::string expanded_message_;
+};
+
+#define fail_check(...) \
+  ONNX_THROW_EX(ONNX_NAMESPACE::checker::ValidationError(ONNX_NAMESPACE::MakeString(__VA_ARGS__)));
+
+class CheckerContext final {
+ public:
+  int get_ir_version() const {
+    return ir_version_;
+  }
+  void set_ir_version(int v) {
+    ir_version_ = v;
+  }
+  const std::unordered_map& get_opset_imports() const {
+    return opset_imports_;
+  }
+  void set_opset_imports(std::unordered_map imps) {
+    opset_imports_ = std::move(imps);
+  }
+  bool is_main_graph() const {
+    return is_main_graph_;
+  }
+  void set_is_main_graph(bool is_main_graph) {
+    is_main_graph_ = is_main_graph;
+  }
+
+  void set_schema_registry(const ISchemaRegistry* schema_registry) {
+    schema_registry_ = schema_registry;
+  }
+
+  const ISchemaRegistry* get_schema_registry() const {
+    return schema_registry_;
+  }
+
+  void set_model_dir(const std::string& model_dir) {
+    model_dir_ = model_dir;
+  }
+
+  std::string get_model_dir() const {
+    return model_dir_;
+  }
+
+  bool skip_opset_compatibility_check() const {
+    return skip_opset_compatibility_check_;
+  }
+
+  void set_skip_opset_compatibility_check(bool value) {
+    skip_opset_compatibility_check_ = value;
+  }
+
+  bool check_custom_domain() const {
+    return check_custom_domain_;
+  }
+
+  void set_check_custom_domain(bool value) {
+    check_custom_domain_ = value;
+  }
+
+  explicit CheckerContext() = default;
+
+ private:
+  int ir_version_{-1};
+  std::unordered_map opset_imports_;
+  bool is_main_graph_ = true;
+  const ISchemaRegistry* schema_registry_ = OpSchemaRegistry::Instance();
+  std::string model_dir_;
+  bool skip_opset_compatibility_check_ = false;
+  bool check_custom_domain_ = false;
+};
+
+class LexicalScopeContext final {
+ public:
+  LexicalScopeContext() = default;
+  ~LexicalScopeContext() = default;
+
+  // Construct an instance with the lexical scope from the parent graph to allow
+  // lookup of names from that scope via this_or_ancestor_graph_has.
+  // The caller must ensure parent_context remains valid for the entire lifetime
+  // of the new instance. Alternatively, if that cannot be guaranteed, create an
+  // instance with the default constructor and populate output_names with the
+  // values from the parent scope so the values are copied instead.
+  LexicalScopeContext(const LexicalScopeContext& parent_context) : parent_context_{&parent_context} {}
+  LexicalScopeContext& operator=(const LexicalScopeContext& parent_context) {
+    if (this == &parent_context) {
+      return *this;
+    }
+    parent_context_ = &parent_context;
+    return *this;
+  }
+  LexicalScopeContext(LexicalScopeContext&&) = delete;
+  LexicalScopeContext& operator=(LexicalScopeContext&&) = delete;
+
+  void add(const std::string& name) {
+    output_names.insert(name);
+  }
+
+  bool this_graph_has(const std::string& name) const {
+    return output_names.count(name) > 0;
+  }
+
+  bool this_or_ancestor_graph_has(const std::string& name) const {
+    return this_graph_has(name) || (parent_context_ && parent_context_->this_or_ancestor_graph_has(name));
+  }
+
+  // public for backwards compatibility. please prefer the public interface of
+  // this class over directly changing output_names
+  std::unordered_set output_names;
+
+ private:
+  const LexicalScopeContext* parent_context_{nullptr};
+};
+
+using IR_VERSION_TYPE = decltype(Version::IR_VERSION);
+void check_value_info(const ValueInfoProto& value_info, const CheckerContext&);
+void check_tensor(const TensorProto& tensor, const CheckerContext&);
+void check_sparse_tensor(const SparseTensorProto& sparse_tensor, const CheckerContext&);
+void check_sequence(const SequenceProto& sequence, const CheckerContext&);
+void check_map(const MapProto& map, const CheckerContext&);
+void check_optional(const OptionalProto& opt, const CheckerContext&);
+void check_attribute(const AttributeProto& attr, const CheckerContext&, const LexicalScopeContext&);
+void check_node(const NodeProto& node, const CheckerContext&, const LexicalScopeContext&);
+void check_graph(const GraphProto& graph, const CheckerContext&, const LexicalScopeContext&);
+void check_function(const FunctionProto& function, const CheckerContext&, const LexicalScopeContext&);
+
+// Check schema compatibility for 2 opset versions for a given node.
+// Checks whether the schema for 2 versions is same, this is true when the opschema
+// does not change between versions.
+void check_opset_compatibility(
+    const NodeProto& node,
+    const CheckerContext& ctx,
+    const std::unordered_map& func_opset_imports,
+    const std::unordered_map& model_opset_imports);
+
+// Checks all model local functions present in ModelProto
+void check_model_local_functions(
+    const ModelProto& model,
+    const CheckerContext& ctx,
+    const LexicalScopeContext& parent_lex);
+
+void check_model(
+    const ModelProto& model,
+    bool full_check = false,
+    bool skip_opset_compatibility_check = false,
+    bool check_custom_domain = false);
+void check_model(
+    const std::string& model_path,
+    bool full_check = false,
+    bool skip_opset_compatibility_check = false,
+    bool check_custom_domain = false);
+std::string resolve_external_data_location(
+    const std::string& base_dir,
+    const std::string& location,
+    const std::string& tensor_name);
+bool check_is_experimental_op(const NodeProto& node);
+
+} // namespace checker
+} // namespace ONNX_NAMESPACE
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/checker.py b/pythonProject/.venv/Lib/site-packages/onnx/checker.py
new file mode 100644
index 0000000000000000000000000000000000000000..b823adb44a31ba411b728b45d3da84ba77cba0d5
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/checker.py
@@ -0,0 +1,172 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Graph utilities for checking whether an ONNX proto message is legal."""
+
+from __future__ import annotations
+
+__all__ = [
+    "check_attribute",
+    "check_function",
+    "check_graph",
+    "check_model",
+    "check_node",
+    "check_sparse_tensor",
+    "check_tensor",
+    "check_value_info",
+    "DEFAULT_CONTEXT",
+    "LEXICAL_SCOPE_CONTEXT",
+    "ValidationError",
+    "C",
+    "MAXIMUM_PROTOBUF",
+]
+
+import os
+import sys
+from typing import TYPE_CHECKING
+
+import onnx.defs
+import onnx.onnx_cpp2py_export.checker as C  # noqa: N812
+from onnx.onnx_pb import IR_VERSION
+
+if TYPE_CHECKING:
+    from google.protobuf.message import Message
+
+# Limitation of single protobuf file is 2GiB
+MAXIMUM_PROTOBUF = 2147483648
+
+
+# NB: Please don't edit this context!
+DEFAULT_CONTEXT = C.CheckerContext()
+DEFAULT_CONTEXT.ir_version = IR_VERSION
+# TODO: Maybe ONNX-ML should also be defaulted?
+DEFAULT_CONTEXT.opset_imports = {"": onnx.defs.onnx_opset_version()}
+
+LEXICAL_SCOPE_CONTEXT = C.LexicalScopeContext()
+
+
+def _ensure_proto_type(proto: Message, proto_type: type[Message]) -> None:
+    if not isinstance(proto, proto_type):
+        raise TypeError(
+            f"The proto message needs to be of type '{proto_type.__name__}'"
+        )
+
+
+def check_value_info(
+    value_info: onnx.ValueInfoProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
+) -> None:
+    _ensure_proto_type(value_info, onnx.ValueInfoProto)
+    return C.check_value_info(value_info.SerializeToString(), ctx)
+
+
+def check_tensor(
+    tensor: onnx.TensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
+) -> None:
+    _ensure_proto_type(tensor, onnx.TensorProto)
+    return C.check_tensor(tensor.SerializeToString(), ctx)
+
+
+def check_attribute(
+    attr: onnx.AttributeProto,
+    ctx: C.CheckerContext = DEFAULT_CONTEXT,
+    lexical_scope_ctx: C.LexicalScopeContext = LEXICAL_SCOPE_CONTEXT,
+) -> None:
+    _ensure_proto_type(attr, onnx.AttributeProto)
+    return C.check_attribute(attr.SerializeToString(), ctx, lexical_scope_ctx)
+
+
+def check_node(
+    node: onnx.NodeProto,
+    ctx: C.CheckerContext = DEFAULT_CONTEXT,
+    lexical_scope_ctx: C.LexicalScopeContext = LEXICAL_SCOPE_CONTEXT,
+) -> None:
+    _ensure_proto_type(node, onnx.NodeProto)
+    return C.check_node(node.SerializeToString(), ctx, lexical_scope_ctx)
+
+
+def check_function(
+    function: onnx.FunctionProto,
+    ctx: C.CheckerContext | None = None,
+    lexical_scope_ctx: C.LexicalScopeContext = LEXICAL_SCOPE_CONTEXT,
+) -> None:
+    _ensure_proto_type(function, onnx.FunctionProto)
+    if ctx is None:
+        ctx = C.CheckerContext()
+        ctx.ir_version = onnx.helper.find_min_ir_version_for(
+            function.opset_import, ignore_unknown=True
+        )
+        ctx.opset_imports = {
+            domain_version.domain: domain_version.version
+            for domain_version in function.opset_import
+        }
+    C.check_function(function.SerializeToString(), ctx, lexical_scope_ctx)
+
+
+def check_graph(
+    graph: onnx.GraphProto,
+    ctx: C.CheckerContext = DEFAULT_CONTEXT,
+    lexical_scope_ctx: C.LexicalScopeContext = LEXICAL_SCOPE_CONTEXT,
+) -> None:
+    _ensure_proto_type(graph, onnx.GraphProto)
+    return C.check_graph(graph.SerializeToString(), ctx, lexical_scope_ctx)
+
+
+def check_sparse_tensor(
+    sparse: onnx.SparseTensorProto, ctx: C.CheckerContext = DEFAULT_CONTEXT
+) -> None:
+    _ensure_proto_type(sparse, onnx.SparseTensorProto)
+    C.check_sparse_tensor(sparse.SerializeToString(), ctx)
+
+
+def check_model(
+    model: onnx.ModelProto | str | bytes | os.PathLike,
+    full_check: bool = False,
+    skip_opset_compatibility_check: bool = False,
+    check_custom_domain: bool = False,
+) -> None:
+    """Check the consistency of a model.
+
+    An exception will be raised if the model's ir_version is not set
+    properly or is higher than checker's ir_version, or if the model
+    has duplicate keys in metadata_props.
+
+    If IR version >= 3, the model must specify opset_import.
+    If IR version < 3, the model cannot have any opset_import specified.
+
+    Args:
+        model: Model to check. If model is a path, the function checks model
+            path first. If the model bytes size is larger than 2GB, function
+            should be called using model path.
+        full_check: If True, the function also runs shape inference check.
+        skip_opset_compatibility_check: If True, the function skips the check for
+            opset compatibility.
+        check_custom_domain: If True, the function will check all domains. Otherwise
+            only check built-in domains.
+    """
+    # If model is a path instead of ModelProto
+    if isinstance(model, (str, os.PathLike)):
+        C.check_model_path(
+            os.fspath(model),
+            full_check,
+            skip_opset_compatibility_check,
+            check_custom_domain,
+        )
+    else:
+        protobuf_string = (
+            model if isinstance(model, bytes) else model.SerializeToString()
+        )
+        # If the protobuf is larger than 2GiB,
+        # remind users should use the model path to check
+        if sys.getsizeof(protobuf_string) > MAXIMUM_PROTOBUF:
+            raise ValueError(
+                "This protobuf of onnx model is too large (>2GiB). Call check_model with model path instead."
+            )
+        C.check_model(
+            protobuf_string,
+            full_check,
+            skip_opset_compatibility_check,
+            check_custom_domain,
+        )
+
+
+ValidationError = C.ValidationError
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/compose.py b/pythonProject/.venv/Lib/site-packages/onnx/compose.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6c76abf5d4835e1e89f46503d62e68b6f1ff797
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/compose.py
@@ -0,0 +1,740 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from onnx import (
+    AttributeProto,
+    GraphProto,
+    ModelProto,
+    TensorProto,
+    checker,
+    helper,
+    utils,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+
+
+def check_overlapping_names(
+    g1: GraphProto, g2: GraphProto, io_map: list[tuple[str, str]] | None = None
+) -> list[tuple[str, list[str]]]:
+    """Checks whether there are name collisions between two graphs
+
+    Returns a list of tuples where the first element represents the member containing overlapping names
+    (One of: "node", "edge", "value_info", "initializer", "sparse_initializer"), and the
+    second element contains a list of names that appear in both graphs on that category.
+
+    Optionally, it takes an io_map, representing the output/inputs to be connected. It provided, overlapping
+    present in the io_map argument will be ignored.
+    """
+    if not isinstance(g1, GraphProto):
+        raise TypeError("g1 argument is not an ONNX graph")
+    if not isinstance(g2, GraphProto):
+        raise TypeError("g2 argument is not an ONNX graph")
+
+    def _overlapping(c1: list[str], c2: list[str]) -> list[str]:
+        return list(set(c1) & set(c2))
+
+    def _edge_names(graph: GraphProto, exclude: set[str] | None = None) -> list[str]:
+        if exclude is None:
+            exclude = set()
+        edges = []
+        for n in graph.node:
+            for i in n.input:
+                if i != "" and i not in exclude:
+                    edges.append(i)  # noqa: PERF401
+            for o in n.output:
+                if o != "" and o not in exclude:
+                    edges.append(o)  # noqa: PERF401
+        return edges
+
+    result = []
+
+    if not io_map:
+        io_map = []
+    io_map_inputs = {elem[1] for elem in io_map}
+
+    # Edges already cover input/output
+    overlap = _overlapping(_edge_names(g1), _edge_names(g2, exclude=io_map_inputs))
+    if overlap:
+        result.append(("edge", overlap))
+
+    overlap = _overlapping(
+        [e.name for e in g1.value_info], [e.name for e in g2.value_info]
+    )
+    if overlap:
+        result.append(("value_info", overlap))
+
+    overlap = _overlapping(
+        [e.name for e in g1.initializer], [e.name for e in g2.initializer]
+    )
+    if overlap:
+        result.append(("initializer", overlap))
+
+    overlap = _overlapping(
+        [e.values.name for e in g1.sparse_initializer],
+        [e.values.name for e in g2.sparse_initializer],
+    ) + _overlapping(
+        [e.indices.name for e in g1.sparse_initializer],
+        [e.indices.name for e in g2.sparse_initializer],
+    )
+    if overlap:
+        result.append(("sparse_initializer", overlap))
+
+    return result
+
+
+def merge_graphs(
+    g1: GraphProto,
+    g2: GraphProto,
+    io_map: list[tuple[str, str]],
+    inputs: list[str] | None = None,
+    outputs: list[str] | None = None,
+    prefix1: str | None = None,
+    prefix2: str | None = None,
+    name: str | None = None,
+    doc_string: str | None = None,
+) -> GraphProto:
+    """Combines two ONNX graphs into a single one.
+
+    The combined graph is defined by connecting the specified set of outputs/inputs. Those inputs/outputs
+    not specified in the io_map argument will remain as inputs/outputs of the combined graph.
+
+    Arguments:
+        g1 (GraphProto): First graph
+        g2 (GraphProto): Second graph
+        io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
+                                          representing outputs of the first graph and inputs of the second
+                                          to be connected
+        inputs (list of string): Optional list of inputs to be included in the combined graph
+                                 By default, all inputs not present in the ``io_map`` argument will be
+                                 included in the combined model
+        outputs (list of string): Optional list of outputs to be included in the combined graph
+                                  By default, all outputs not present in the ``io_map`` argument will be
+                                  included in the combined model
+        prefix1 (string): Optional prefix to be added to all names in g1
+        prefix2 (string): Optional prefix to be added to all names in g2
+        name (string): Optional name for the combined graph
+                       By default, the name is g1.name and g2.name concatenated with an underscore delimiter
+        doc_string (string): Optional docstring for the combined graph
+                             If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
+
+    Returns:
+        GraphProto
+    """
+    if not isinstance(g1, GraphProto):
+        raise TypeError("g1 argument is not an ONNX graph")
+    if not isinstance(g2, GraphProto):
+        raise TypeError("g2 argument is not an ONNX graph")
+
+    # Prefixing names in the graph if requested, adjusting io_map accordingly
+    if prefix1 or prefix2:
+        if prefix1:
+            g1_copy = GraphProto()
+            g1_copy.CopyFrom(g1)
+            g1 = g1_copy
+            g1 = add_prefix_graph(g1, prefix=prefix1)
+        if prefix2:
+            g2_copy = GraphProto()
+            g2_copy.CopyFrom(g2)
+            g2 = g2_copy
+            g2 = add_prefix_graph(g2, prefix=prefix2)
+        io_map = [
+            (
+                prefix1 + io[0] if prefix1 else io[0],
+                prefix2 + io[1] if prefix2 else io[1],
+            )
+            for io in io_map
+        ]
+
+    io_map_g1_outs = {io[0] for io in io_map}
+    io_map_g2_ins = {io[1] for io in io_map}
+    reversed_io_map = {in_name: out_name for out_name, in_name in io_map}
+    g1_outs = {o.name for o in g1.output}
+    g2_ins = {i.name for i in g2.input}
+
+    # If necessary extract subgraphs
+    if inputs or outputs:
+        if not inputs:
+            g1_inputs = [i.name for i in g1.input]
+            g2_inputs = [i.name for i in g2.input]
+        else:
+            input_set = set(inputs)
+            g1_inputs = [i.name for i in g1.input if i.name in input_set]
+            g2_inputs = [
+                i.name
+                for i in g2.input
+                if i.name in input_set or i.name in io_map_g2_ins
+            ]
+
+        if not outputs:
+            g1_outputs = [o.name for o in g1.output]
+            g2_outputs = [o.name for o in g2.output]
+        else:
+            output_set = set(outputs)
+            g1_outputs = [
+                o.name
+                for o in g1.output
+                if o.name in output_set or o.name in io_map_g1_outs
+            ]
+            g2_outputs = [o.name for o in g2.output if o.name in output_set]
+
+        if len(g1_inputs) < len(g1.input) or len(g1_outputs) < len(g1.output):
+            e1 = utils.Extractor(helper.make_model(g1))
+            g1 = e1.extract_model(g1_inputs, g1_outputs).graph
+
+        if len(g2_inputs) < len(g2.input) or len(g2_outputs) < len(g2.output):
+            e2 = utils.Extractor(helper.make_model(g2))
+            g2 = e2.extract_model(g2_inputs, g2_outputs).graph
+
+    # Check that input/output names specified in the io_map argument are valid input/output names
+    for g1_out_name, g2_in_name in io_map:
+        if g1_out_name not in g1_outs:
+            raise ValueError(f"Output {g1_out_name} is not present in g1")
+        if g2_in_name not in g2_ins:
+            raise ValueError(f"Input {g2_in_name} is not present in g2")
+
+    # Check for name collision
+    overlapping_names = check_overlapping_names(g1, g2, io_map)
+    if len(overlapping_names) > 0:
+        category, names = overlapping_names[0]
+        raise ValueError(
+            "Cant merge two graphs with overlapping names. "
+            f"Found repeated {category} names: "
+            + ", ".join(names)
+            + "\n"
+            + "Consider using ``onnx.compose.add_prefix`` to add a prefix to names in one of the graphs."
+        )
+
+    g = GraphProto()
+
+    g.node.extend(g1.node)
+    g2_nodes_begin = len(g.node)
+    g.node.extend(g2.node)
+    g2_nodes_end = len(g.node)
+
+    # Search inputs of the subgraph recursively
+    def connect_io(sub_graph: GraphProto, start: int, end: int) -> None:
+        for node_idx in range(start, end):
+            node = sub_graph.node[node_idx]
+            for attr in node.attribute:
+                if attr.type == AttributeProto.GRAPH:
+                    connect_io(attr.g, 0, len(attr.g.node))
+
+            for index, name_ in enumerate(node.input):
+                if name_ in reversed_io_map:
+                    node.input[index] = reversed_io_map[name_]
+
+    # Connecting outputs of the first graph with the inputs of the second
+    connect_io(g, g2_nodes_begin, g2_nodes_end)
+
+    if inputs:
+        input_set = set(inputs)
+        g.input.extend([i for i in g1.input if i.name in input_set])
+        g.input.extend([i for i in g2.input if i.name in input_set])
+    else:
+        g.input.extend(g1.input)
+        g.input.extend([i for i in g2.input if i.name not in io_map_g2_ins])
+
+    if outputs:
+        output_set = set(outputs)
+        g.output.extend([o for o in g1.output if o.name in output_set])
+        g.output.extend([o for o in g2.output if o.name in output_set])
+    else:
+        g.output.extend([o for o in g1.output if o.name not in io_map_g1_outs])
+        g.output.extend(g2.output)
+
+    g.initializer.extend(g1.initializer)
+    g.initializer.extend(
+        [init for init in g2.initializer if init.name not in io_map_g2_ins]
+    )
+
+    g.sparse_initializer.extend(g1.sparse_initializer)
+    g.sparse_initializer.extend(
+        [
+            init
+            for init in g2.sparse_initializer
+            if init.values.name not in io_map_g2_ins
+        ]
+    )
+
+    g.value_info.extend(g1.value_info)
+    g.value_info.extend([vi for vi in g2.value_info if vi.name not in io_map_g2_ins])
+
+    g.name = name if name is not None else "_".join([g1.name, g2.name])
+
+    if doc_string is None:
+        doc_string = (
+            f"Graph combining {g1.name} and {g2.name}\n"
+            + g1.name
+            + "\n\n"
+            + g1.doc_string
+            + "\n\n"
+            + g2.name
+            + "\n\n"
+            + g2.doc_string
+        )
+    g.doc_string = doc_string
+
+    return g
+
+
+def merge_models(
+    m1: ModelProto,
+    m2: ModelProto,
+    io_map: list[tuple[str, str]],
+    inputs: list[str] | None = None,
+    outputs: list[str] | None = None,
+    prefix1: str | None = None,
+    prefix2: str | None = None,
+    name: str | None = None,
+    doc_string: str | None = None,
+    producer_name: str | None = "onnx.compose.merge_models",
+    producer_version: str | None = "1.0",
+    domain: str | None = "",
+    model_version: int | None = 1,
+) -> ModelProto:
+    """Combines two ONNX models into a single one.
+
+    The combined model is defined by connecting the specified set of outputs/inputs.
+    Those inputs/outputs not specified in the io_map argument will remain as
+    inputs/outputs of the combined model.
+
+    Both models should have the same IR version, and same operator sets imported.
+
+    Arguments:
+        m1 (ModelProto): First model
+        m2 (ModelProto): Second model
+        io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
+                                          representing outputs of the first graph and inputs of the second
+                                          to be connected
+        inputs (list of string): Optional list of inputs to be included in the combined graph
+                                 By default, all inputs not present in the ``io_map`` argument will be
+                                 included in the combined model
+        outputs (list of string): Optional list of outputs to be included in the combined graph
+                                  By default, all outputs not present in the ``io_map`` argument will be
+                                  included in the combined model
+        prefix1 (string): Optional prefix to be added to all names in m1
+        prefix2 (string): Optional prefix to be added to all names in m2
+        name (string): Optional name for the combined graph
+                       By default, the name is g1.name and g2.name concatenated with an underscore delimiter
+        doc_string (string): Optional docstring for the combined graph
+                             If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
+        producer_name (string): Optional producer name for the combined model. Default: 'onnx.compose'
+        producer_version (string): Optional producer version for the combined model. Default: "1.0"
+        domain (string): Optional domain of the combined model. Default: ""
+        model_version (int): Optional version of the graph encoded. Default: 1
+
+    Returns:
+        ModelProto
+    """
+    if not isinstance(m1, ModelProto):
+        raise TypeError("m1 argument is not an ONNX model")
+    if not isinstance(m2, ModelProto):
+        raise TypeError("m2 argument is not an ONNX model")
+
+    if m1.ir_version != m2.ir_version:
+        raise ValueError(
+            f"IR version mismatch {m1.ir_version} != {m2.ir_version}."
+            " Both models should have the same IR version"
+        )
+    ir_version = m1.ir_version
+
+    opset_import_map: MutableMapping[str, int] = {}
+    opset_imports = list(m1.opset_import) + list(m2.opset_import)
+
+    for entry in opset_imports:
+        if entry.domain in opset_import_map:
+            found_version = opset_import_map[entry.domain]
+            if entry.version != found_version:
+                raise ValueError(
+                    "Can't merge two models with different operator set ids for a given domain. "
+                    f"Got: {m1.opset_import} and {m2.opset_import}"
+                )
+        else:
+            opset_import_map[entry.domain] = entry.version
+
+    # Prefixing names in the graph if requested, adjusting io_map accordingly
+    if prefix1 or prefix2:
+        if prefix1:
+            m1_copy = ModelProto()
+            m1_copy.CopyFrom(m1)
+            m1 = m1_copy
+            m1 = add_prefix(m1, prefix=prefix1)
+        if prefix2:
+            m2_copy = ModelProto()
+            m2_copy.CopyFrom(m2)
+            m2 = m2_copy
+            m2 = add_prefix(m2, prefix=prefix2)
+        io_map = [
+            (
+                prefix1 + io[0] if prefix1 else io[0],
+                prefix2 + io[1] if prefix2 else io[1],
+            )
+            for io in io_map
+        ]
+
+    graph = merge_graphs(
+        m1.graph,
+        m2.graph,
+        io_map,
+        inputs=inputs,
+        outputs=outputs,
+        name=name,
+        doc_string=doc_string,
+    )
+    model = helper.make_model(
+        graph,
+        producer_name=producer_name,
+        producer_version=producer_version,
+        domain=domain,
+        model_version=model_version,
+        opset_imports=opset_imports,
+        ir_version=ir_version,
+    )
+
+    # Merging model metadata props
+    model_props = {}
+    for meta_entry in m1.metadata_props:
+        model_props[meta_entry.key] = meta_entry.value
+    for meta_entry in m2.metadata_props:
+        if meta_entry.key in model_props:
+            value = model_props[meta_entry.key]
+            if value != meta_entry.value:
+                raise ValueError(
+                    "Can't merge models with different values for the same model metadata property."
+                    f" Found: property = {meta_entry.key}, with values {value} and {meta_entry.value}."
+                )
+        else:
+            model_props[meta_entry.key] = meta_entry.value
+    helper.set_model_props(model, model_props)
+
+    # Merging functions
+    function_overlap = list(
+        {f.name for f in m1.functions} & {f.name for f in m2.functions}
+    )
+    if function_overlap:
+        raise ValueError(
+            "Can't merge models with overlapping local function names."
+            " Found in both graphs: " + ", ".join(function_overlap)
+        )
+    model.functions.MergeFrom(m1.functions)
+    model.functions.MergeFrom(m2.functions)
+
+    checker.check_model(model)
+    return model
+
+
+def add_prefix_graph(
+    graph: GraphProto,
+    prefix: str,
+    rename_nodes: bool | None = True,
+    rename_edges: bool | None = True,
+    rename_inputs: bool | None = True,
+    rename_outputs: bool | None = True,
+    rename_initializers: bool | None = True,
+    rename_value_infos: bool | None = True,
+    inplace: bool | None = False,
+    name_map: dict[str, str] | None = None,
+) -> GraphProto:
+    """Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
+    initializers, sparse initializer, value infos.
+
+    It can be used as a utility before merging graphs that have overlapping names.
+    Empty names are not prefixed.
+
+    Arguments:
+        graph (GraphProto): Graph
+        prefix (str): Prefix to be added to each name in the graph
+        rename_nodes (bool): Whether to prefix node names
+        rename_edges (bool): Whether to prefix node edge names
+        rename_inputs (bool): Whether to prefix input names
+        rename_outputs (bool): Whether to prefix output names
+        rename_initializers (bool): Whether to prefix initializer and sparse initializer names
+        rename_value_infos (bool): Whether to prefix value info names
+        inplace (bool): If True, mutates the graph directly.
+                        Otherwise, a copy will be created
+        name_map: (Dict): shared name_map in subgraph
+
+    Returns:
+        GraphProto
+    """
+    if not isinstance(graph, GraphProto):
+        raise TypeError("graph argument is not an ONNX graph")
+
+    if not inplace:
+        g = GraphProto()
+        g.CopyFrom(graph)
+    else:
+        g = graph
+
+    def _prefixed(prefix: str, name: str) -> str:
+        return prefix + name if len(name) > 0 else name
+
+    if name_map is None:
+        name_map = {}
+
+    if rename_edges:
+        # See https://github.com/onnx/onnx/pull/6869#issuecomment-2852719536.
+        # Consider only intermediate nodes, that are not connected to graph outputs.
+        # Rename graph inputs or outputs separately based on rename_inputs/rename_outputs flags.
+        graph_output_names = {o.name for o in g.output}
+        for n in g.node:
+            for e in n.output:
+                if e not in graph_output_names:
+                    name_map[e] = _prefixed(prefix, e)
+
+    if rename_inputs:
+        for entry in g.input:
+            name_map[entry.name] = _prefixed(prefix, entry.name)
+    if rename_outputs:
+        for entry in g.output:
+            name_map[entry.name] = _prefixed(prefix, entry.name)
+
+    if rename_nodes:
+        for n in g.node:
+            n.name = _prefixed(prefix, n.name)
+            for attribute in n.attribute:
+                if attribute.g:
+                    add_prefix_graph(
+                        attribute.g, prefix, inplace=True, name_map=name_map
+                    )
+
+    if rename_initializers:
+        for init in g.initializer:
+            name_map[init.name] = _prefixed(prefix, init.name)
+        for sparse_init in g.sparse_initializer:
+            name_map[sparse_init.values.name] = _prefixed(
+                prefix, sparse_init.values.name
+            )
+            name_map[sparse_init.indices.name] = _prefixed(
+                prefix, sparse_init.indices.name
+            )
+
+    if rename_value_infos:
+        for entry in g.value_info:
+            name_map[entry.name] = _prefixed(prefix, entry.name)
+
+    for n in g.node:
+        for i, output in enumerate(n.output):
+            if n.output[i] in name_map:
+                n.output[i] = name_map[output]
+        for i, input_ in enumerate(n.input):
+            if n.input[i] in name_map:
+                n.input[i] = name_map[input_]
+
+    for in_desc in g.input:
+        if in_desc.name in name_map:
+            in_desc.name = name_map[in_desc.name]
+    for out_desc in g.output:
+        if out_desc.name in name_map:
+            out_desc.name = name_map[out_desc.name]
+
+    for initializer in g.initializer:
+        if initializer.name in name_map:
+            initializer.name = name_map[initializer.name]
+    for sparse_initializer in g.sparse_initializer:
+        if sparse_initializer.values.name in name_map:
+            sparse_initializer.values.name = name_map[sparse_initializer.values.name]
+        if sparse_initializer.indices.name in name_map:
+            sparse_initializer.indices.name = name_map[sparse_initializer.indices.name]
+
+    for value_info in g.value_info:
+        if value_info.name in name_map:
+            value_info.name = name_map[value_info.name]
+
+    return g
+
+
+def add_prefix(
+    model: ModelProto,
+    prefix: str,
+    rename_nodes: bool | None = True,
+    rename_edges: bool | None = True,
+    rename_inputs: bool | None = True,
+    rename_outputs: bool | None = True,
+    rename_initializers: bool | None = True,
+    rename_value_infos: bool | None = True,
+    rename_functions: bool | None = True,
+    inplace: bool | None = False,
+) -> ModelProto:
+    """Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
+    initializers, sparse initializer, value infos, and local functions.
+
+    It can be used as a utility before merging graphs that have overlapping names.
+    Empty names are not _prefixed.
+
+    Arguments:
+        model (ModelProto): Model
+        prefix (str): Prefix to be added to each name in the graph
+        rename_nodes (bool): Whether to prefix node names
+        rename_edges (bool): Whether to prefix node edge names
+        rename_inputs (bool): Whether to prefix input names
+        rename_outputs (bool): Whether to prefix output names
+        rename_initializers (bool): Whether to prefix initializer and sparse initializer names
+        rename_value_infos (bool): Whether to prefix value info nanes
+        rename_functions (bool): Whether to prefix local function names
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        ModelProto
+    """
+    if not isinstance(model, ModelProto):
+        raise TypeError("model argument is not an ONNX model")
+
+    if not inplace:
+        m = ModelProto()
+        m.CopyFrom(model)
+        model = m
+
+    add_prefix_graph(
+        model.graph,
+        prefix,
+        rename_nodes=rename_nodes,
+        rename_edges=rename_edges,
+        rename_inputs=rename_inputs,
+        rename_outputs=rename_outputs,
+        rename_initializers=rename_initializers,
+        rename_value_infos=rename_value_infos,
+        inplace=True,  # No need to create a copy, since it's a new model
+    )
+
+    if rename_functions:
+        f_name_map = {}
+        for f in model.functions:
+            new_f_name = prefix + f.name
+            f_name_map[f.name] = new_f_name
+            f.name = new_f_name
+        # Adjust references to local functions in other local function
+        # definitions
+        for f in model.functions:
+            for n in f.node:
+                if n.op_type in f_name_map:
+                    n.op_type = f_name_map[n.op_type]
+        # Adjust references to local functions in the graph
+        for n in model.graph.node:
+            if n.op_type in f_name_map:
+                n.op_type = f_name_map[n.op_type]
+
+    return model
+
+
+def expand_out_dim_graph(
+    graph: GraphProto,
+    dim_idx: int,
+    inplace: bool | None = False,
+) -> GraphProto:
+    """Inserts an extra dimension with extent 1 to each output in the graph.
+
+    Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
+    for example when the second one expects a batch dimension.
+
+    Arguments:
+        graph (GraphProto): Graph
+        dim_idx (int): Index of the dimension to be inserted.
+                       A negative value means counting dimensions from the back.
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        GraphProto
+    """
+    if not isinstance(graph, GraphProto):
+        raise TypeError("graph argument is not an ONNX graph")
+
+    if not inplace:
+        g = GraphProto()
+        g.CopyFrom(graph)
+    else:
+        g = graph
+
+    orig_out_names = [output.name for output in g.output]
+
+    for n in g.node:
+        for i, out in enumerate(n.output):
+            if out in orig_out_names:
+                n.output[i] = out + f"_collapsed_dim_{dim_idx}"
+        for i, inp in enumerate(n.input):
+            if inp in orig_out_names:
+                n.input[i] = inp + f"_collapsed_dim_{dim_idx}"
+
+    expand_dim_k = g.name + "_expand_out_dim_idx"
+    g.node.append(
+        helper.make_node(
+            "Constant",
+            inputs=[],
+            outputs=[expand_dim_k],
+            name=f"{expand_dim_k}-constant",
+            value=helper.make_tensor(
+                name=f"{expand_dim_k}-value",
+                data_type=TensorProto.INT64,
+                dims=[
+                    1,
+                ],
+                vals=[
+                    dim_idx,
+                ],
+            ),
+        )
+    )
+
+    for _ in range(len(g.output)):
+        o = g.output.pop(0)
+        prev_output = o.name + f"_collapsed_dim_{dim_idx}"
+        g.node.append(
+            helper.make_node(
+                "Unsqueeze",
+                inputs=[prev_output, expand_dim_k],
+                outputs=[o.name],
+                name=f"unsqueeze-{o.name}",
+            )
+        )
+        new_shape = [d.dim_value for d in o.type.tensor_type.shape.dim]
+        new_shape.insert(dim_idx, 1)
+        g.output.append(
+            helper.make_tensor_value_info(
+                o.name, o.type.tensor_type.elem_type, new_shape
+            )
+        )
+    return g
+
+
+def expand_out_dim(
+    model: ModelProto,
+    dim_idx: int,
+    inplace: bool | None = False,
+) -> ModelProto:
+    """Inserts an extra dimension with extent 1 to each output in the graph.
+
+    Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
+    for example when the second one expects a batch dimension.
+
+    Arguments:
+        model (ModelProto): Model
+        dim_idx (int): Index of the dimension to be inserted.
+                       A negative value means counting dimensions from the back.
+        inplace (bool): If True, mutates the model directly.
+                        Otherwise, a copy will be created
+
+    Returns:
+        ModelProto
+    """
+    if not isinstance(model, ModelProto):
+        raise TypeError("model argument is not an ONNX model")
+
+    if not inplace:
+        m = ModelProto()
+        m.CopyFrom(model)
+        model = m
+
+    expand_out_dim_graph(
+        model.graph,
+        dim_idx,
+        inplace=True,  # No need to create a copy, since it's a new model
+    )
+    return model
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/cpp2py_export.cc b/pythonProject/.venv/Lib/site-packages/onnx/cpp2py_export.cc
new file mode 100644
index 0000000000000000000000000000000000000000..45ef5f1f9330332e2b1638ce53783bf68517ce3d
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/cpp2py_export.cc
@@ -0,0 +1,856 @@
+// Copyright (c) ONNX Project Contributors
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#include 
+#include 
+#include 
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include "onnx/checker.h"
+#include "onnx/common/ir_pb_converter.h"
+#include "onnx/defs/parser.h"
+#include "onnx/defs/printer.h"
+#include "onnx/defs/schema.h"
+#include "onnx/defs/shape_inference.h"
+#include "onnx/inliner/inliner.h"
+#include "onnx/py_utils.h"
+#include "onnx/shape_inference/implementation.h"
+#include "onnx/version_converter/convert.h"
+
+#if (PYBIND11_VERSION_MAJOR != 2 || PYBIND11_VERSION_MINOR < 12)
+#pragma error "Pybind11 must be >= 2.12 to be compatible with numpy 2.0."
+#endif
+
+#ifndef PYBIND11_DETAILED_ERROR_MESSAGES
+#define PYBIND11_DETAILED_ERROR_MESSAGES
+#endif
+
+template 
+struct PythonProtoTypeMap {};
+
+#define DEFINE_PROTO_TYPE_MAP(_ProtoType, PY_MODULE_NAME, PY_TYPE_NAME)                             \
+  template <>                                                                                       \
+  struct PythonProtoTypeMap<_ProtoType> {                                                           \
+    static constexpr auto FullName = pybind11::detail::const_name(PY_MODULE_NAME "." PY_TYPE_NAME); \
+    static constexpr auto TypeName = pybind11::detail::const_name(PY_TYPE_NAME);                    \
+    static constexpr auto ModuleName = pybind11::detail::const_name(PY_MODULE_NAME);                \
+  };
+
+#ifdef ONNX_USE_LITE_PROTO
+using BASE_PROTO_TYPE = ::google::protobuf::MessageLite;
+#else
+using BASE_PROTO_TYPE = ::google::protobuf::Message;
+#endif
+
+template 
+class pybind11::detail::type_caster<_ProtoType, std::enable_if_t::value>> {
+ public:
+  PYBIND11_TYPE_CASTER(_ProtoType, PythonProtoTypeMap<_ProtoType>::FullName);
+  bool load(handle py_proto, bool) {
+    try {
+      if (!pybind11::hasattr(py_proto, "SerializeToString")) {
+        return false;
+      }
+      pybind11::bytes serialized = py_proto.attr("SerializeToString")();
+      std::string serialized_str = serialized;
+      if (!value.ParseFromString(serialized_str)) {
+        return false;
+      }
+      return true;
+    } catch (const pybind11::error_already_set&) {
+      return false;
+    }
+  }
+  static handle cast(const _ProtoType& cpp_proto, return_value_policy /* policy */, handle /* parent */) {
+    auto py_proto = pybind11::module::import(PythonProtoTypeMap<_ProtoType>::ModuleName.text)
+                        .attr(PythonProtoTypeMap<_ProtoType>::TypeName.text)();
+    std::string serialized = cpp_proto.SerializeAsString();
+    py_proto.attr("ParseFromString")(pybind11::bytes(serialized));
+    return py_proto.release();
+  }
+};
+
+DEFINE_PROTO_TYPE_MAP(ONNX_NAMESPACE::AttributeProto, "onnx", "AttributeProto");
+DEFINE_PROTO_TYPE_MAP(ONNX_NAMESPACE::TypeProto, "onnx", "TypeProto");
+DEFINE_PROTO_TYPE_MAP(ONNX_NAMESPACE::TensorProto, "onnx", "TensorProto");
+DEFINE_PROTO_TYPE_MAP(ONNX_NAMESPACE::SparseTensorProto, "onnx", "SparseTensorProto");
+DEFINE_PROTO_TYPE_MAP(ONNX_NAMESPACE::TensorShapeProto, "onnx", "TensorShapeProto");
+
+namespace ONNX_NAMESPACE {
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+template 
+static std::tuple Parse(const char* cstr) {
+  ProtoType proto{};
+  OnnxParser parser(cstr);
+  auto status = parser.Parse(proto);
+  std::string out;
+  proto.SerializeToString(&out);
+  return std::make_tuple(status.IsOK(), py::bytes(status.ErrorMessage()), py::bytes(out));
+}
+
+template 
+static std::string ProtoBytesToText(const py::bytes& bytes) {
+  ProtoType proto{};
+  ParseProtoFromPyBytes(&proto, bytes);
+  return ProtoToString(proto);
+}
+
+template >
+static std::pair, std::unordered_map> ParseProtoFromBytesMap(
+    const std::unordered_map& bytesMap) {
+  std::vector values(bytesMap.size());
+  std::unordered_map result;
+  size_t i = 0;
+  for (const auto& kv : bytesMap) {
+    ParseProtoFromPyBytes(&values[i], kv.second);
+    result[kv.first] = &values[i];
+    i++;
+  }
+  // C++ guarantees that the pointers remain valid after std::vector is moved.
+  return std::make_pair(std::move(values), result);
+}
+
+static std::unordered_map CallNodeInferenceFunction(
+    OpSchema* schema,
+    const py::bytes& nodeBytes,
+    const std::unordered_map& valueTypesByNameBytes,
+    const std::unordered_map& inputDataByNameBytes,
+    const std::unordered_map& inputSparseDataByNameBytes,
+    std::unordered_map opsetImports,
+    const int irVersion) {
+  NodeProto node{};
+  ParseProtoFromPyBytes(&node, nodeBytes);
+  // Early fail if node is badly defined - may throw ValidationError
+  schema->Verify(node);
+
+  // Convert arguments to C++ types, allocating memory
+  const auto& valueTypes = ParseProtoFromBytesMap(valueTypesByNameBytes);
+  const auto& inputData = ParseProtoFromBytesMap(inputDataByNameBytes);
+  const auto& inputSparseData = ParseProtoFromBytesMap(inputSparseDataByNameBytes);
+  if (opsetImports.empty()) {
+    opsetImports[schema->domain()] = schema->SinceVersion();
+  }
+
+  shape_inference::GraphInferenceContext graphInferenceContext(
+      valueTypes.second, opsetImports, nullptr, {}, OpSchemaRegistry::Instance(), nullptr, irVersion);
+  // Construct inference context and get results - may throw InferenceError
+  // TODO: if it is desirable for infer_node_outputs to provide check_type, strict_mode, data_prop,
+  // we can add them to the Python API. For now we just assume the default options.
+  ShapeInferenceOptions options{false, 0, false};
+  shape_inference::InferenceContextImpl ctx(
+      node, valueTypes.second, inputData.second, inputSparseData.second, options, nullptr, &graphInferenceContext);
+  schema->GetTypeAndShapeInferenceFunction()(ctx);
+  // Verify the inference succeeded - may also throw ValidationError
+  // Note that input types were not validated until now (except that their count was correct)
+  schema->CheckInputOutputType(ctx);
+
+  // Convert back into bytes returned to Python
+  std::unordered_map typeProtoBytes;
+  for (size_t i = 0; i < ctx.allOutputTypes_.size(); i++) {
+    const auto& proto = ctx.allOutputTypes_[i];
+    if (proto.IsInitialized()) {
+      std::string s;
+      proto.SerializeToString(&s);
+      typeProtoBytes[node.output(static_cast(i))] = py::bytes(s);
+    }
+  }
+
+  return typeProtoBytes;
+}
+
+template 
+static std::tuple, std::vector> ConvertPyObjToPtr(const std::vector& pyObjs) {
+  std::vector objs;
+  std::vector ptrs;
+  objs.reserve(pyObjs.size());
+  ptrs.reserve(pyObjs.size());
+  for (const auto& obj : pyObjs) {
+    if (obj.is_none()) {
+      ptrs.push_back(nullptr);
+      continue;
+    }
+    objs.emplace_back(obj.cast());
+    ptrs.push_back(&objs.back());
+  }
+  return std::make_tuple(std::move(objs), std::move(ptrs));
+}
+
+PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
+  onnx_cpp2py_export.doc() = "Python interface to ONNX";
+
+  onnx_cpp2py_export.attr("ONNX_ML") = py::bool_(
+#ifdef ONNX_ML
+      true
+#else // ONNX_ML
+      false
+#endif // ONNX_ML
+  );
+
+  // Avoid Segmentation fault if we not free the python function in Custom Schema
+  onnx_cpp2py_export.add_object("_cleanup", py::capsule([] { OpSchemaRegistry::OpSchemaDeregisterAll(); }));
+
+  // Submodule `schema`
+  auto defs = onnx_cpp2py_export.def_submodule("defs");
+  defs.doc() = "Schema submodule";
+  py::register_exception(defs, "SchemaError");
+
+  py::class_ op_schema(defs, "OpSchema", "Schema of an operator.");
+
+  // Define the class enums first because they are used as default values in function definitions
+  py::enum_(op_schema, "FormalParameterOption")
+      .value("Single", OpSchema::Single)
+      .value("Optional", OpSchema::Optional)
+      .value("Variadic", OpSchema::Variadic);
+
+  py::enum_(op_schema, "DifferentiationCategory")
+      .value("Unknown", OpSchema::Unknown)
+      .value("Differentiable", OpSchema::Differentiable)
+      .value("NonDifferentiable", OpSchema::NonDifferentiable);
+
+  py::enum_(op_schema, "AttrType")
+      .value("FLOAT", AttributeProto::FLOAT)
+      .value("INT", AttributeProto::INT)
+      .value("STRING", AttributeProto::STRING)
+      .value("TENSOR", AttributeProto::TENSOR)
+      .value("GRAPH", AttributeProto::GRAPH)
+      .value("FLOATS", AttributeProto::FLOATS)
+      .value("INTS", AttributeProto::INTS)
+      .value("STRINGS", AttributeProto::STRINGS)
+      .value("TENSORS", AttributeProto::TENSORS)
+      .value("GRAPHS", AttributeProto::GRAPHS)
+      .value("SPARSE_TENSOR", AttributeProto::SPARSE_TENSOR)
+      .value("SPARSE_TENSORS", AttributeProto::SPARSE_TENSORS)
+      .value("TYPE_PROTO", AttributeProto::TYPE_PROTO)
+      .value("TYPE_PROTOS", AttributeProto::TYPE_PROTOS);
+
+  py::enum_(op_schema, "SupportType")
+      .value("COMMON", OpSchema::SupportType::COMMON)
+      .value("EXPERIMENTAL", OpSchema::SupportType::EXPERIMENTAL);
+
+  py::class_(op_schema, "Attribute")
+      .def(
+          py::init([](std::string name, AttributeProto::AttributeType type, std::string description, bool required) {
+            // Construct an attribute.
+            // Use a lambda to swap the order of the arguments to match the Python API
+            return OpSchema::Attribute(std::move(name), std::move(description), type, required);
+          }),
+          py::arg("name"),
+          py::arg("type"),
+          py::arg("description") = "",
+          py::kw_only(),
+          py::arg("required") = true)
+      .def(
+          py::init([](std::string name, const py::object& default_value, std::string description) {
+            // Construct an attribute with a default value.
+            // Attributes with default values are not required
+            auto bytes = default_value.attr("SerializeToString")().cast();
+            AttributeProto proto{};
+            ParseProtoFromPyBytes(&proto, bytes);
+            return OpSchema::Attribute(std::move(name), std::move(description), std::move(proto));
+          }),
+          py::arg("name"),
+          py::arg("default_value"), // type: onnx.AttributeProto
+          py::arg("description") = "")
+      .def_readonly("name", &OpSchema::Attribute::name)
+      .def_readonly("description", &OpSchema::Attribute::description)
+      .def_readonly("type", &OpSchema::Attribute::type)
+      .def_property_readonly(
+          "_default_value",
+          [](OpSchema::Attribute* attr) -> py::bytes {
+            std::string out;
+            attr->default_value.SerializeToString(&out);
+            return out;
+          })
+      .def_readonly("required", &OpSchema::Attribute::required);
+
+  py::class_(op_schema, "TypeConstraintParam")
+      .def(
+          py::init, std::string>(),
+          py::arg("type_param_str"),
+          py::arg("allowed_type_strs"),
+          py::arg("description") = "")
+      .def_readonly("type_param_str", &OpSchema::TypeConstraintParam::type_param_str)
+      .def_readonly("allowed_type_strs", &OpSchema::TypeConstraintParam::allowed_type_strs)
+      .def_readonly("description", &OpSchema::TypeConstraintParam::description);
+
+  py::class_(op_schema, "FormalParameter")
+      .def(
+          py::init([](std::string name,
+                      std::string type_str,
+                      const std::string& description,
+                      OpSchema::FormalParameterOption param_option,
+                      bool is_homogeneous,
+                      int min_arity,
+                      OpSchema::DifferentiationCategory differentiation_category) {
+            // Use a lambda to swap the order of the arguments to match the Python API
+            return OpSchema::FormalParameter(
+                std::move(name),
+                description,
+                std::move(type_str),
+                param_option,
+                is_homogeneous,
+                min_arity,
+                differentiation_category);
+          }),
+          py::arg("name"),
+          py::arg("type_str"),
+          py::arg("description") = "",
+          py::kw_only(),
+          py::arg("param_option") = OpSchema::Single,
+          py::arg("is_homogeneous") = true,
+          py::arg("min_arity") = 1,
+          py::arg("differentiation_category") = OpSchema::DifferentiationCategory::Unknown)
+
+      .def_property_readonly("name", &OpSchema::FormalParameter::GetName)
+      .def_property_readonly("types", &OpSchema::FormalParameter::GetTypes)
+      .def_property_readonly("type_str", &OpSchema::FormalParameter::GetTypeStr)
+      .def_property_readonly("description", &OpSchema::FormalParameter::GetDescription)
+      .def_property_readonly("option", &OpSchema::FormalParameter::GetOption)
+      .def_property_readonly("is_homogeneous", &OpSchema::FormalParameter::GetIsHomogeneous)
+      .def_property_readonly("min_arity", &OpSchema::FormalParameter::GetMinArity)
+      .def_property_readonly("differentiation_category", &OpSchema::FormalParameter::GetDifferentiationCategory);
+
+  op_schema
+      .def(
+          py::init([](std::string name,
+                      std::string domain,
+                      int since_version,
+                      const std::string& doc,
+                      std::vector inputs,
+                      std::vector outputs,
+                      std::vector, std::string>> type_constraints,
+                      std::vector attributes) {
+            auto self = OpSchema();
+
+            self.SetName(std::move(name)).SetDomain(std::move(domain)).SinceVersion(since_version).SetDoc(doc);
+            // Add inputs and outputs
+            for (auto i = 0; i < inputs.size(); ++i) {
+              self.Input(i, std::move(inputs[i]));
+            }
+            for (auto i = 0; i < outputs.size(); ++i) {
+              self.Output(i, std::move(outputs[i]));
+            }
+            // Add type constraints
+            for (auto& type_constraint : type_constraints) {
+              std::string type_str;
+              std::vector constraints;
+              std::string description;
+              tie(type_str, constraints, description) = std::move(type_constraint);
+              self.TypeConstraint(std::move(type_str), std::move(constraints), std::move(description));
+            }
+            // Add attributes
+            for (auto& attribute : attributes) {
+              self.Attr(std::move(attribute));
+            }
+
+            self.Finalize();
+
+            return self;
+          }),
+          py::arg("name"),
+          py::arg("domain"),
+          py::arg("since_version"),
+          py::arg("doc") = "",
+          py::kw_only(),
+          py::arg("inputs") = std::vector{},
+          py::arg("outputs") = std::vector{},
+          py::arg("type_constraints") = std::vector /* constraints */,
+              std::string /* description */>>{},
+          py::arg("attributes") = std::vector{})
+      .def_property("name", &OpSchema::Name, [](OpSchema& self, const std::string& name) { self.SetName(name); })
+      .def_property(
+          "domain", &OpSchema::domain, [](OpSchema& self, const std::string& domain) { self.SetDomain(domain); })
+      .def_property("doc", &OpSchema::doc, [](OpSchema& self, const std::string& doc) { self.SetDoc(doc); })
+      .def_property_readonly("file", &OpSchema::file)
+      .def_property_readonly("line", &OpSchema::line)
+      .def_property_readonly("support_level", &OpSchema::support_level)
+      .def_property_readonly("since_version", &OpSchema::since_version)
+      .def_property_readonly("deprecated", &OpSchema::deprecated)
+      .def_property_readonly("function_opset_versions", &OpSchema::function_opset_versions)
+      .def_property_readonly(
+          "context_dependent_function_opset_versions", &OpSchema::context_dependent_function_opset_versions)
+      .def_property_readonly(
+          "all_function_opset_versions",
+          [](OpSchema* op) -> std::vector {
+            std::vector all_function_opset_versions = op->function_opset_versions();
+            std::vector context_dependent_function_opset_versions =
+                op->context_dependent_function_opset_versions();
+            all_function_opset_versions.insert(
+                all_function_opset_versions.end(),
+                context_dependent_function_opset_versions.begin(),
+                context_dependent_function_opset_versions.end());
+            std::sort(all_function_opset_versions.begin(), all_function_opset_versions.end());
+            all_function_opset_versions.erase(
+                std::unique(all_function_opset_versions.begin(), all_function_opset_versions.end()),
+                all_function_opset_versions.end());
+            return all_function_opset_versions;
+          })
+      .def_property_readonly("min_input", &OpSchema::min_input)
+      .def_property_readonly("max_input", &OpSchema::max_input)
+      .def_property_readonly("min_output", &OpSchema::min_output)
+      .def_property_readonly("max_output", &OpSchema::max_output)
+      .def_property_readonly("attributes", &OpSchema::attributes)
+      .def_property_readonly("inputs", &OpSchema::inputs)
+      .def_property_readonly("outputs", &OpSchema::outputs)
+      .def_property_readonly("has_type_and_shape_inference_function", &OpSchema::has_type_and_shape_inference_function)
+      .def_property_readonly("has_data_propagation_function", &OpSchema::has_data_propagation_function)
+      .def_property_readonly("type_constraints", &OpSchema::typeConstraintParams)
+      .def_static("is_infinite", [](int v) { return v == std::numeric_limits::max(); })
+      .def(
+          "_infer_node_outputs",
+          CallNodeInferenceFunction,
+          py::arg("nodeBytes"),
+          py::arg("valueTypesByNameBytes"),
+          py::arg("inputDataByNameBytes") = std::unordered_map{},
+          py::arg("inputSparseDataByNameBytes") = std::unordered_map{},
+          py::arg("opsetImports") = std::unordered_map{},
+          py::arg("irVersion") = int(IR_VERSION))
+      .def_property_readonly("has_function", &OpSchema::HasFunction)
+      .def_property_readonly(
+          "_function_body",
+          [](OpSchema* op) -> py::bytes {
+            std::string bytes = "";
+            if (op->HasFunction())
+              op->GetFunction()->SerializeToString(&bytes);
+            return py::bytes(bytes);
+          })
+      .def(
+          "get_function_with_opset_version",
+          [](OpSchema* op, int opset_version) -> py::bytes {
+            std::string bytes = "";
+            const FunctionProto* function_proto = op->GetFunction(opset_version);
+            if (function_proto) {
+              function_proto->SerializeToString(&bytes);
+            }
+            return py::bytes(bytes);
+          })
+      .def_property_readonly("has_context_dependent_function", &OpSchema::HasContextDependentFunction)
+      .def(
+          "get_context_dependent_function",
+          [](OpSchema* op, const py::bytes& bytes, const std::vector& input_types_bytes) -> py::bytes {
+            NodeProto proto{};
+            ParseProtoFromPyBytes(&proto, bytes);
+            std::string func_bytes = "";
+            if (op->HasContextDependentFunction()) {
+              std::vector input_types;
+              input_types.reserve(input_types_bytes.size());
+              for (auto& type_bytes : input_types_bytes) {
+                TypeProto type_proto{};
+                ParseProtoFromPyBytes(&type_proto, type_bytes);
+                input_types.push_back(type_proto);
+              }
+              FunctionBodyBuildContextImpl ctx(proto, input_types);
+              FunctionProto func_proto;
+              op->BuildContextDependentFunction(ctx, func_proto);
+              func_proto.SerializeToString(&func_bytes);
+            }
+            return py::bytes(func_bytes);
+          })
+      .def(
+          "get_context_dependent_function_with_opset_version",
+          [](OpSchema* op, int opset_version, const py::bytes& bytes, const std::vector& input_types_bytes)
+              -> py::bytes {
+            NodeProto proto{};
+            ParseProtoFromPyBytes(&proto, bytes);
+            std::string func_bytes = "";
+            if (op->HasContextDependentFunctionWithOpsetVersion(opset_version)) {
+              std::vector input_types;
+              input_types.reserve(input_types_bytes.size());
+              for (auto& type_bytes : input_types_bytes) {
+                TypeProto type_proto{};
+                ParseProtoFromPyBytes(&type_proto, type_bytes);
+                input_types.push_back(type_proto);
+              }
+              FunctionBodyBuildContextImpl ctx(proto, input_types);
+              FunctionProto func_proto;
+              op->BuildContextDependentFunction(ctx, func_proto, opset_version);
+              func_proto.SerializeToString(&func_bytes);
+            }
+            return py::bytes(func_bytes);
+          })
+      .def(
+          "set_type_and_shape_inference_function",
+          [](OpSchema& op, const std::function& func) -> OpSchema& {
+            auto wrapper = [=](InferenceContext& ctx) { func(&ctx); };
+            return op.TypeAndShapeInferenceFunction(wrapper);
+          },
+          py::return_value_policy::reference_internal)
+      .def("get_type_and_shape_inference_function", &OpSchema::GetTypeAndShapeInferenceFunction);
+
+  defs.def(
+          "has_schema",
+          [](const std::string& op_type, const std::string& domain) -> bool {
+            return OpSchemaRegistry::Schema(op_type, domain) != nullptr;
+          },
+          "op_type"_a,
+          "domain"_a = ONNX_DOMAIN)
+      .def(
+          "has_schema",
+          [](const std::string& op_type, int max_inclusive_version, const std::string& domain) -> bool {
+            return OpSchemaRegistry::Schema(op_type, max_inclusive_version, domain) != nullptr;
+          },
+          "op_type"_a,
+          "max_inclusive_version"_a,
+          "domain"_a = ONNX_DOMAIN)
+      .def(
+          "schema_version_map",
+          []() -> std::unordered_map> {
+            return OpSchemaRegistry::DomainToVersionRange::Instance().Map();
+          })
+      .def(
+          "get_schema",
+          [](const std::string& op_type, const int max_inclusive_version, const std::string& domain) -> OpSchema {
+            const auto* schema = OpSchemaRegistry::Schema(op_type, max_inclusive_version, domain);
+            if (!schema) {
+              fail_schema(
+                  "No schema registered for '" + op_type + "' version '" + std::to_string(max_inclusive_version) +
+                  "' and domain '" + domain + "'!");
+            }
+            return *schema;
+          },
+          "op_type"_a,
+          "max_inclusive_version"_a,
+          "domain"_a = ONNX_DOMAIN,
+          "Return the schema of the operator *op_type* and for a specific version.")
+      .def(
+          "get_schema",
+          [](const std::string& op_type, const std::string& domain) -> OpSchema {
+            const auto* schema = OpSchemaRegistry::Schema(op_type, domain);
+            if (!schema) {
+              fail_schema("No schema registered for '" + op_type + "' and domain '" + domain + "'!");
+            }
+            return *schema;
+          },
+          "op_type"_a,
+          "domain"_a = ONNX_DOMAIN,
+          "Return the schema of the operator *op_type* and for a specific version.")
+      .def(
+          "get_all_schemas",
+          []() -> const std::vector { return OpSchemaRegistry::get_all_schemas(); },
+          "Return the schema of all existing operators for the latest version.")
+      .def(
+          "get_all_schemas_with_history",
+          []() -> const std::vector { return OpSchemaRegistry::get_all_schemas_with_history(); },
+          "Return the schema of all existing operators and all versions.")
+      .def(
+          "set_domain_to_version",
+          [](const std::string& domain, int min_version, int max_version, int last_release_version) {
+            auto& obj = OpSchemaRegistry::DomainToVersionRange::Instance();
+            if (obj.Map().count(domain) == 0) {
+              obj.AddDomainToVersion(domain, min_version, max_version, last_release_version);
+            } else {
+              obj.UpdateDomainToVersion(domain, min_version, max_version, last_release_version);
+            }
+          },
+          "domain"_a,
+          "min_version"_a,
+          "max_version"_a,
+          "last_release_version"_a = -1,
+          "Set the version range and last release version of the specified domain.")
+      .def(
+          "register_schema",
+          [](OpSchema schema) { RegisterSchema(std::move(schema), 0, true, true); },
+          "schema"_a,
+          "Register a user provided OpSchema.")
+      .def(
+          "deregister_schema",
+          &DeregisterSchema,
+          "op_type"_a,
+          "version"_a,
+          "domain"_a,
+          "Deregister the specified OpSchema.");
+
+  // Submodule `checker`
+  auto checker = onnx_cpp2py_export.def_submodule("checker");
+  checker.doc() = "Checker submodule";
+
+  py::class_ checker_context(checker, "CheckerContext");
+  checker_context.def(py::init<>())
+      .def_property("ir_version", &checker::CheckerContext::get_ir_version, &checker::CheckerContext::set_ir_version)
+      .def_property(
+          "opset_imports", &checker::CheckerContext::get_opset_imports, &checker::CheckerContext::set_opset_imports);
+
+  py::class_ lexical_scope_context(checker, "LexicalScopeContext");
+  lexical_scope_context.def(py::init<>());
+
+  py::register_exception(checker, "ValidationError");
+
+  checker.def("check_value_info", [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+    ValueInfoProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    checker::check_value_info(proto, ctx);
+  });
+
+  checker.def("check_tensor", [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+    TensorProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    checker::check_tensor(proto, ctx);
+  });
+
+  checker.def("check_sparse_tensor", [](const py::bytes& bytes, const checker::CheckerContext& ctx) -> void {
+    SparseTensorProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    checker::check_sparse_tensor(proto, ctx);
+  });
+
+  checker.def(
+      "check_attribute",
+      [](const py::bytes& bytes,
+         const checker::CheckerContext& ctx,
+         const checker::LexicalScopeContext& lex_ctx) -> void {
+        AttributeProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_attribute(proto, ctx, lex_ctx);
+      });
+
+  checker.def(
+      "check_node",
+      [](const py::bytes& bytes,
+         const checker::CheckerContext& ctx,
+         const checker::LexicalScopeContext& lex_ctx) -> void {
+        NodeProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_node(proto, ctx, lex_ctx);
+      });
+
+  checker.def(
+      "check_function",
+      [](const py::bytes& bytes,
+         const checker::CheckerContext& ctx,
+         const checker::LexicalScopeContext& lex_ctx) -> void {
+        FunctionProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_function(proto, ctx, lex_ctx);
+      });
+
+  checker.def(
+      "check_graph",
+      [](const py::bytes& bytes,
+         const checker::CheckerContext& ctx,
+         const checker::LexicalScopeContext& lex_ctx) -> void {
+        GraphProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_graph(proto, ctx, lex_ctx);
+      });
+
+  checker.def(
+      "check_model",
+      [](const py::bytes& bytes, bool full_check, bool skip_opset_compatibility_check, bool check_custom_domain)
+          -> void {
+        ModelProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        checker::check_model(proto, full_check, skip_opset_compatibility_check, check_custom_domain);
+      },
+      "bytes"_a,
+      "full_check"_a = false,
+      "skip_opset_compatibility_check"_a = false,
+      "check_custom_domain"_a = false);
+
+  checker.def(
+      "check_model_path",
+      (void (*)(
+          const std::string& path,
+          bool full_check,
+          bool skip_opset_compatibility_check,
+          bool check_custom_domain))&checker::check_model,
+      "path"_a,
+      "full_check"_a = false,
+      "skip_opset_compatibility_check"_a = false,
+      "check_custom_domain"_a = false);
+
+  checker.def("_resolve_external_data_location", &checker::resolve_external_data_location);
+
+  // Submodule `version_converter`
+  auto version_converter = onnx_cpp2py_export.def_submodule("version_converter");
+  version_converter.doc() = "VersionConverter submodule";
+  py::register_exception(version_converter, "ConvertError");
+
+  version_converter.def("convert_version", [](const py::bytes& bytes, const py::int_& target) {
+    ModelProto proto{};
+    ParseProtoFromPyBytes(&proto, bytes);
+    shape_inference::InferShapes(proto);
+    auto result = version_conversion::ConvertVersion(proto, target);
+    std::string out;
+    result.SerializeToString(&out);
+    return py::bytes(out);
+  });
+
+  // Submodule `inliner`
+  auto inliner = onnx_cpp2py_export.def_submodule("inliner");
+  inliner.doc() = "Inliner submodule";
+
+  inliner.def("inline_local_functions", [](const py::bytes& bytes, bool convert_version) {
+    ModelProto model{};
+    ParseProtoFromPyBytes(&model, bytes);
+    inliner::InlineLocalFunctions(model, convert_version);
+    std::string out;
+    model.SerializeToString(&out);
+    return py::bytes(out);
+  });
+
+  // inline_selected_functions: Inlines all functions specified in function_ids, unless
+  // exclude is true, in which case it inlines all functions except those specified in
+  // function_ids.
+  inliner.def(
+      "inline_selected_functions",
+      [](const py::bytes& bytes, std::vector> function_ids, bool exclude) {
+        ModelProto model{};
+        ParseProtoFromPyBytes(&model, bytes);
+        auto function_id_set = inliner::FunctionIdSet::Create(std::move(function_ids), exclude);
+        inliner::InlineSelectedLocalFunctions(model, *function_id_set);
+        std::string out;
+        model.SerializeToString(&out);
+        return py::bytes(out);
+      });
+
+  inliner.def(
+      "inline_selected_functions2",
+      [](const py::bytes& bytes, std::vector> function_ids, bool exclude) {
+        ModelProto model{};
+        ParseProtoFromPyBytes(&model, bytes);
+        auto function_id_set = inliner::FunctionIdSet::Create(std::move(function_ids), exclude);
+        inliner::InlineSelectedFunctions(model, *function_id_set, nullptr);
+        std::string out;
+        model.SerializeToString(&out);
+        return py::bytes(out);
+      });
+
+  // Submodule `shape_inference`
+  auto shape_inference = onnx_cpp2py_export.def_submodule("shape_inference");
+  shape_inference.doc() = "Shape Inference submodule";
+  py::register_exception(shape_inference, "InferenceError");
+
+  py::class_ inference_context(shape_inference, "InferenceContext", "Inference context");
+
+  inference_context.def("get_attribute", &InferenceContext::getAttribute);
+  inference_context.def("get_num_inputs", &InferenceContext::getNumInputs);
+  inference_context.def("get_input_type", &InferenceContext::getInputType);
+  inference_context.def("has_input", &InferenceContext::hasInput);
+  inference_context.def("get_input_data", &InferenceContext::getInputData);
+  inference_context.def("get_num_outputs", &InferenceContext::getNumOutputs);
+  inference_context.def("get_output_type", &InferenceContext::getOutputType);
+  inference_context.def("set_output_type", [](InferenceContext& self, size_t idx, const TypeProto& src) {
+    auto* dst = self.getOutputType(idx);
+    if (dst == nullptr) {
+      return false;
+    }
+    dst->CopyFrom(src);
+    return true;
+  });
+  inference_context.def("has_output", &InferenceContext::hasOutput);
+  inference_context.def(
+      "get_graph_attribute_inferencer",
+      &InferenceContext::getGraphAttributeInferencer,
+      py::return_value_policy::reference_internal);
+  inference_context.def("get_input_sparse_data", &InferenceContext::getInputSparseData);
+  inference_context.def("get_symbolic_input", &InferenceContext::getSymbolicInput);
+  inference_context.def("get_display_name", &InferenceContext::getDisplayName);
+
+  py::class_ graph_inferencer(shape_inference, "GraphInferencer", "Graph Inferencer");
+  graph_inferencer.def(
+      "do_inferencing",
+      [](GraphInferencer& self,
+         const std::vector& inputTypesObj,
+         const std::vector& inputDataObj) {
+        auto inputTypesTuple = ConvertPyObjToPtr(inputTypesObj);
+        auto inputDataTuple = ConvertPyObjToPtr(inputDataObj);
+        auto ret = self.doInferencing(std::get<1>(inputTypesTuple), std::get<1>(inputDataTuple));
+        std::vector ret_obj(ret.size());
+        for (size_t i = 0; i < ret.size(); ++i) {
+          ret_obj[i] = py::cast(ret[i]);
+        }
+        return ret_obj;
+      });
+
+  shape_inference.def(
+      "infer_shapes",
+      [](const py::bytes& bytes, bool check_type, bool strict_mode, bool data_prop) {
+        ModelProto proto{};
+        ParseProtoFromPyBytes(&proto, bytes);
+        ShapeInferenceOptions options{check_type, strict_mode ? 1 : 0, data_prop};
+        shape_inference::InferShapes(proto, OpSchemaRegistry::Instance(), options);
+        std::string out;
+        proto.SerializeToString(&out);
+        return py::bytes(out);
+      },
+      "bytes"_a,
+      "check_type"_a = false,
+      "strict_mode"_a = false,
+      "data_prop"_a = false);
+
+  shape_inference.def(
+      "infer_shapes_path",
+      [](const std::string& model_path,
+         const std::string& output_path,
+         bool check_type,
+         bool strict_mode,
+         bool data_prop) -> void {
+        ShapeInferenceOptions options{check_type, strict_mode ? 1 : 0, data_prop};
+        shape_inference::InferShapes(model_path, output_path, OpSchemaRegistry::Instance(), options);
+      });
+
+  shape_inference.def(
+      "infer_function_output_types",
+      [](const py::bytes& function_proto_bytes,
+         const std::vector& input_types_bytes,
+         const std::vector& attributes_bytes) -> std::vector {
+        FunctionProto proto{};
+        ParseProtoFromPyBytes(&proto, function_proto_bytes);
+
+        std::vector input_types;
+        input_types.reserve(input_types_bytes.size());
+        for (const py::bytes& bytes : input_types_bytes) {
+          TypeProto type;
+          ParseProtoFromPyBytes(&type, bytes);
+          input_types.push_back(type);
+        }
+
+        std::vector attributes;
+        attributes.reserve(attributes_bytes.size());
+        for (const py::bytes& bytes : attributes_bytes) {
+          AttributeProto attr;
+          ParseProtoFromPyBytes(&attr, bytes);
+          attributes.push_back(attr);
+        }
+
+        std::vector output_types = shape_inference::InferFunctionOutputTypes(proto, input_types, attributes);
+        std::vector result;
+        result.reserve(output_types.size());
+        for (auto& type_proto : output_types) {
+          std::string out;
+          type_proto.SerializeToString(&out);
+          result.emplace_back(out);
+        }
+        return result;
+      });
+
+  // Submodule `parser`
+  auto parser = onnx_cpp2py_export.def_submodule("parser");
+  parser.doc() = "Parser submodule";
+
+  parser.def("parse_model", Parse);
+  parser.def("parse_graph", Parse);
+  parser.def("parse_function", Parse);
+  parser.def("parse_node", Parse);
+
+  // Submodule `printer`
+  auto printer = onnx_cpp2py_export.def_submodule("printer");
+  printer.doc() = "Printer submodule";
+
+  printer.def("model_to_text", ProtoBytesToText);
+  printer.def("function_to_text", ProtoBytesToText);
+  printer.def("graph_to_text", ProtoBytesToText);
+}
+
+} // namespace ONNX_NAMESPACE
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/external_data_helper.py b/pythonProject/.venv/Lib/site-packages/onnx/external_data_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..a23ff15d29b29c826a527fb7d6c9755f70e4a3ca
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/external_data_helper.py
@@ -0,0 +1,330 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import os
+import re
+import sys
+import uuid
+from itertools import chain
+from typing import TYPE_CHECKING, Callable
+
+import onnx.onnx_cpp2py_export.checker as c_checker
+from onnx.onnx_pb import (
+    AttributeProto,
+    FunctionProto,
+    GraphProto,
+    ModelProto,
+    TensorProto,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+class ExternalDataInfo:
+    def __init__(self, tensor: TensorProto) -> None:
+        self.location = ""
+        self.offset = None
+        self.length = None
+        self.checksum = None
+        self.basepath = ""
+
+        for entry in tensor.external_data:
+            setattr(self, entry.key, entry.value)
+
+        if self.offset:
+            self.offset = int(self.offset)
+
+        if self.length:
+            self.length = int(self.length)
+
+
+def load_external_data_for_tensor(tensor: TensorProto, base_dir: str) -> None:
+    """Loads data from an external file for tensor.
+    Ideally TensorProto should not hold any raw data but if it does it will be ignored.
+
+    Arguments:
+        tensor: a TensorProto object.
+        base_dir: directory that contains the external data.
+    """
+    info = ExternalDataInfo(tensor)
+    external_data_file_path = c_checker._resolve_external_data_location(  # type: ignore[attr-defined]
+        base_dir, info.location, tensor.name
+    )
+    with open(external_data_file_path, "rb") as data_file:
+        if info.offset:
+            data_file.seek(info.offset)
+
+        if info.length:
+            tensor.raw_data = data_file.read(info.length)
+        else:
+            tensor.raw_data = data_file.read()
+
+
+def load_external_data_for_model(model: ModelProto, base_dir: str) -> None:
+    """Loads external tensors into model
+
+    Arguments:
+        model: ModelProto to load external data to
+        base_dir: directory that contains external data
+    """
+    for tensor in _get_all_tensors(model):
+        if uses_external_data(tensor):
+            load_external_data_for_tensor(tensor, base_dir)
+            # After loading raw_data from external_data, change the state of tensors
+            tensor.data_location = TensorProto.DEFAULT
+            # and remove external data
+            del tensor.external_data[:]
+
+
+def set_external_data(
+    tensor: TensorProto,
+    location: str,
+    offset: int | None = None,
+    length: int | None = None,
+    checksum: str | None = None,
+    basepath: str | None = None,
+) -> None:
+    if not tensor.HasField("raw_data"):
+        raise ValueError(
+            "Tensor "
+            + tensor.name
+            + "does not have raw_data field. Cannot set external data for this tensor."
+        )
+
+    del tensor.external_data[:]
+    tensor.data_location = TensorProto.EXTERNAL
+    for k, v in {
+        "location": location,
+        "offset": int(offset) if offset is not None else None,
+        "length": int(length) if length is not None else None,
+        "checksum": checksum,
+        "basepath": basepath,
+    }.items():
+        if v is not None:
+            entry = tensor.external_data.add()
+            entry.key = k
+            entry.value = str(v)
+
+
+def convert_model_to_external_data(
+    model: ModelProto,
+    all_tensors_to_one_file: bool = True,
+    location: str | None = None,
+    size_threshold: int = 1024,
+    convert_attribute: bool = False,
+) -> None:
+    """Call to set all tensors with raw data as external data. This call should precede 'save_model'.
+    'save_model' saves all the tensors data as external data after calling this function.
+
+    Arguments:
+        model (ModelProto): Model to be converted.
+        all_tensors_to_one_file (bool): If true, save all tensors to one external file specified by location.
+            If false, save each tensor to a file named with the tensor name.
+        location: specify the external file relative to the model that all tensors to save to.
+            Path is relative to the model path.
+            If not specified, will use the model name.
+        size_threshold: Threshold for size of data. Only when tensor's data is >= the size_threshold
+            it will be converted to external data. To convert every tensor with raw data to external data set size_threshold=0.
+        convert_attribute (bool): If true, convert all tensors to external data
+                       If false, convert only non-attribute tensors to external data
+
+    Raise:
+        ValueError: If location is not a relative path.
+        FileExistsError: If a file already exists in location.
+    """
+    tensors = _get_initializer_tensors(model)
+    if convert_attribute:
+        tensors = _get_all_tensors(model)
+
+    if all_tensors_to_one_file:
+        file_name = str(uuid.uuid1()) + ".data"
+        if location:
+            if os.path.isabs(location):
+                raise ValueError(
+                    "location must be a relative path that is relative to the model path."
+                )
+            if os.path.exists(location):
+                raise FileExistsError(f"External data file exists in {location}.")
+            file_name = location
+        for tensor in tensors:
+            if (
+                tensor.HasField("raw_data")
+                and sys.getsizeof(tensor.raw_data) >= size_threshold
+            ):
+                set_external_data(tensor, file_name)
+    else:
+        for tensor in tensors:
+            if (
+                tensor.HasField("raw_data")
+                and sys.getsizeof(tensor.raw_data) >= size_threshold
+            ):
+                tensor_location = tensor.name
+                if not _is_valid_filename(tensor_location):
+                    tensor_location = str(uuid.uuid1())
+                set_external_data(tensor, tensor_location)
+
+
+def convert_model_from_external_data(model: ModelProto) -> None:
+    """Call to set all tensors which use external data as embedded data.
+    save_model saves all the tensors data as embedded data after
+    calling this function.
+
+    Arguments:
+        model (ModelProto): Model to be converted.
+    """
+    for tensor in _get_all_tensors(model):
+        if uses_external_data(tensor):
+            if not tensor.HasField("raw_data"):
+                raise ValueError("raw_data field doesn't exist.")
+            del tensor.external_data[:]
+            tensor.data_location = TensorProto.DEFAULT
+
+
+def save_external_data(tensor: TensorProto, base_path: str) -> None:
+    """Writes tensor data to an external file according to information in the `external_data` field.
+
+    Arguments:
+        tensor (TensorProto): Tensor object to be serialized
+        base_path: System path of a folder where tensor data is to be stored
+    """
+    info = ExternalDataInfo(tensor)
+    external_data_file_path = os.path.join(base_path, info.location)
+
+    # Retrieve the tensor's data from raw_data or load external file
+    if not tensor.HasField("raw_data"):
+        raise ValueError("raw_data field doesn't exist.")
+
+    # Create file if it doesn't exist
+    if not os.path.isfile(external_data_file_path):
+        with open(external_data_file_path, "ab"):
+            pass
+
+    # Open file for reading and writing at random locations ('r+b')
+    with open(external_data_file_path, "r+b") as data_file:
+        data_file.seek(0, 2)
+        if info.offset is not None:
+            # Pad file to required offset if needed
+            file_size = data_file.tell()
+            if info.offset > file_size:
+                data_file.write(b"\0" * (info.offset - file_size))
+
+            data_file.seek(info.offset)
+        offset = data_file.tell()
+        data_file.write(tensor.raw_data)
+        set_external_data(tensor, info.location, offset, data_file.tell() - offset)
+
+
+def _get_all_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Scan an ONNX model for all tensors and return as an iterator."""
+    return chain(
+        _get_initializer_tensors(onnx_model_proto),
+        _get_attribute_tensors(onnx_model_proto),
+    )
+
+
+def _recursive_attribute_processor(
+    attribute: AttributeProto, func: Callable[[GraphProto], Iterable[TensorProto]]
+) -> Iterable[TensorProto]:
+    """Create an iterator through processing ONNX model attributes with functor."""
+    if attribute.type == AttributeProto.GRAPH:
+        yield from func(attribute.g)
+    if attribute.type == AttributeProto.GRAPHS:
+        for graph in attribute.graphs:
+            yield from func(graph)
+
+
+def _get_initializer_tensors_from_graph(
+    graph_or_function: GraphProto | FunctionProto, /
+) -> Iterable[TensorProto]:
+    """Create an iterator of initializer tensors from ONNX model graph/function."""
+    if isinstance(graph_or_function, GraphProto):
+        yield from graph_or_function.initializer
+    for node in graph_or_function.node:
+        for attribute in node.attribute:
+            yield from _recursive_attribute_processor(
+                attribute, _get_initializer_tensors_from_graph
+            )
+
+
+def _get_initializer_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Create an iterator of initializer tensors from ONNX model."""
+    yield from _get_initializer_tensors_from_graph(onnx_model_proto.graph)
+    for function in onnx_model_proto.functions:
+        yield from _get_attribute_tensors_from_graph(function)
+
+
+def _get_attribute_tensors_from_graph(
+    graph_or_function: GraphProto | FunctionProto, /
+) -> Iterable[TensorProto]:
+    """Create an iterator of tensors from node attributes of an ONNX model graph/function."""
+    for node in graph_or_function.node:
+        for attribute in node.attribute:
+            if attribute.HasField("t"):
+                yield attribute.t
+            yield from attribute.tensors
+            yield from _recursive_attribute_processor(
+                attribute, _get_attribute_tensors_from_graph
+            )
+
+
+def _get_attribute_tensors(onnx_model_proto: ModelProto) -> Iterable[TensorProto]:
+    """Create an iterator of tensors from node attributes of an ONNX model."""
+    yield from _get_attribute_tensors_from_graph(onnx_model_proto.graph)
+    for function in onnx_model_proto.functions:
+        yield from _get_attribute_tensors_from_graph(function)
+
+
+def _is_valid_filename(filename: str) -> bool:
+    """Utility to check whether the provided filename is valid."""
+    exp = re.compile('^[^<>:;,?"*|/]+$')
+    match = exp.match(filename)
+    return bool(match)
+
+
+def uses_external_data(tensor: TensorProto) -> bool:
+    """Returns true if the tensor stores data in an external location."""
+    return (
+        tensor.HasField("data_location")
+        and tensor.data_location == TensorProto.EXTERNAL
+    )
+
+
+def remove_external_data_field(tensor: TensorProto, field_key: str) -> None:
+    """Removes a field from a Tensor's external_data key-value store.
+
+    Modifies tensor object in place.
+
+    Arguments:
+        tensor (TensorProto): Tensor object from which value will be removed
+        field_key (string): The key of the field to be removed
+    """
+    for i, field in enumerate(tensor.external_data):
+        if field.key == field_key:
+            del tensor.external_data[i]
+
+
+def write_external_data_tensors(model: ModelProto, filepath: str) -> ModelProto:
+    """Serializes data for all the tensors which have data location set to TensorProto.External.
+
+    Note: This function also strips basepath information from all tensors' external_data fields.
+
+    Arguments:
+        model (ModelProto): Model object which is the source of tensors to serialize.
+        filepath: System path to the directory which should be treated as base path for external data.
+
+    Returns:
+        ModelProto: The modified model object.
+    """
+    for tensor in _get_all_tensors(model):
+        # Writing to external data happens in 2 passes:
+        # 1. Tensors with raw data which pass the necessary conditions (size threshold etc) are marked for serialization
+        # 2. The raw data in these tensors is serialized to a file
+        # Thus serialize only if tensor has raw data and it was marked for serialization
+        if uses_external_data(tensor) and tensor.HasField("raw_data"):
+            save_external_data(tensor, filepath)
+            tensor.ClearField("raw_data")
+
+    return model
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/gen_proto.py b/pythonProject/.venv/Lib/site-packages/onnx/gen_proto.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b8b3d98ab6537e173407ffa2d7c68c3d2d617e7
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/gen_proto.py
@@ -0,0 +1,270 @@
+#!/usr/bin/env python
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import argparse
+import glob
+import os
+import re
+import subprocess
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+autogen_header = """\
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+"""
+
+LITE_OPTION = """
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
+"""
+
+DEFAULT_PACKAGE_NAME = "onnx"
+
+IF_ONNX_ML_REGEX = re.compile(r"\s*//\s*#if\s+ONNX-ML\s*$")
+ENDIF_ONNX_ML_REGEX = re.compile(r"\s*//\s*#endif\s*$")
+ELSE_ONNX_ML_REGEX = re.compile(r"\s*//\s*#else\s*$")
+
+
+def process_ifs(lines: Iterable[str], onnx_ml: bool) -> Iterable[str]:
+    in_if = 0
+    for line in lines:
+        if IF_ONNX_ML_REGEX.match(line):
+            assert in_if == 0
+            in_if = 1
+        elif ELSE_ONNX_ML_REGEX.match(line):
+            assert in_if == 1
+            in_if = 2
+        elif ENDIF_ONNX_ML_REGEX.match(line):
+            assert in_if in (1, 2)
+            in_if = 0
+        else:  # noqa: PLR5501
+            if in_if == 0:
+                yield line
+            elif in_if == 1 and onnx_ml:
+                yield line
+            elif in_if == 2 and not onnx_ml:  # noqa: PLR2004
+                yield line
+
+
+IMPORT_REGEX = re.compile(r'(\s*)import\s*"([^"]*)\.proto";\s*$')
+PACKAGE_NAME_REGEX = re.compile(r"\{PACKAGE_NAME\}")
+ML_REGEX = re.compile(r"(.*)\-ml")
+
+
+def process_package_name(lines: Iterable[str], package_name: str) -> Iterable[str]:
+    need_rename = package_name != DEFAULT_PACKAGE_NAME
+    for line in lines:
+        m = IMPORT_REGEX.match(line) if need_rename else None
+        if m:
+            include_name = m.group(2)
+            ml = ML_REGEX.match(include_name)
+            if ml:
+                include_name = f"{ml.group(1)}_{package_name}-ml"
+            else:
+                include_name = f"{include_name}_{package_name}"
+            yield m.group(1) + f'import "{include_name}.proto";'
+        else:
+            yield PACKAGE_NAME_REGEX.sub(package_name, line)
+
+
+PROTO_SYNTAX_REGEX = re.compile(r'(\s*)syntax\s*=\s*"proto2"\s*;\s*$')
+OPTIONAL_REGEX = re.compile(r"(\s*)optional\s(.*)$")
+
+
+def convert_to_proto3(lines: Iterable[str]) -> Iterable[str]:
+    for line in lines:
+        # Set the syntax specifier
+        m = PROTO_SYNTAX_REGEX.match(line)
+        if m:
+            yield m.group(1) + 'syntax = "proto3";'
+            continue
+
+        # Remove optional keywords
+        m = OPTIONAL_REGEX.match(line)
+        if m:
+            yield m.group(1) + m.group(2)
+            continue
+
+        # Rewrite import
+        m = IMPORT_REGEX.match(line)
+        if m:
+            yield m.group(1) + f'import "{m.group(2)}.proto3";'
+            continue
+
+        yield line
+
+
+def gen_proto3_code(
+    protoc_path: str, proto3_path: str, include_path: str, cpp_out: str, python_out: str
+) -> None:
+    print(f"Generate pb3 code using {protoc_path}")
+    build_args = [protoc_path, proto3_path, "-I", include_path]
+    build_args.extend(["--cpp_out", cpp_out, "--python_out", python_out])
+    subprocess.check_call(build_args)
+
+
+def translate(source: str, proto: int, onnx_ml: bool, package_name: str) -> str:
+    lines: Iterable[str] = source.splitlines()
+    lines = process_ifs(lines, onnx_ml=onnx_ml)
+    lines = process_package_name(lines, package_name=package_name)
+    if proto == 3:  # noqa: PLR2004
+        lines = convert_to_proto3(lines)
+    else:
+        assert proto == 2  # noqa: PLR2004
+    return os.linesep.join(lines)
+
+
+def qualify(f: str, pardir: str | None = None) -> str:
+    if pardir is None:
+        pardir = os.path.realpath(os.path.dirname(__file__))
+    return os.path.join(pardir, f)
+
+
+def convert(
+    stem: str,
+    package_name: str,
+    output: str,
+    do_onnx_ml: bool = False,
+    lite: bool = False,
+    protoc_path: str = "",
+) -> None:
+    proto_in = qualify(f"{stem}.in.proto")
+    need_rename = package_name != DEFAULT_PACKAGE_NAME
+    # Having a separate variable for import_ml ensures that the import statements for the generated
+    # proto files can be set separately from the ONNX_ML environment variable setting.
+    import_ml = do_onnx_ml
+    # We do not want to generate the onnx-data-ml.proto files for onnx-data.in.proto,
+    # as there is no change between onnx-data.proto and the ML version.
+    if "onnx-data" in proto_in:
+        do_onnx_ml = False
+    if do_onnx_ml:
+        proto_base = f"{stem}_{package_name}-ml" if need_rename else f"{stem}-ml"
+    else:
+        proto_base = f"{stem}_{package_name}" if need_rename else f"{stem}"
+    proto = qualify(f"{proto_base}.proto", pardir=output)
+    proto3 = qualify(f"{proto_base}.proto3", pardir=output)
+
+    print(f"Processing {proto_in}")
+    with open(proto_in, encoding="utf-8") as fin:
+        source = fin.read()
+        print(f"Writing {proto}")
+        with open(proto, "w", newline="", encoding="utf-8") as fout:
+            fout.write(autogen_header)
+            fout.write(
+                translate(source, proto=2, onnx_ml=import_ml, package_name=package_name)
+            )
+            if lite:
+                fout.write(LITE_OPTION)
+        print(f"Writing {proto3}")
+        with open(proto3, "w", newline="", encoding="utf-8") as fout:
+            fout.write(autogen_header)
+            fout.write(
+                translate(source, proto=3, onnx_ml=import_ml, package_name=package_name)
+            )
+            if lite:
+                fout.write(LITE_OPTION)
+        if protoc_path:
+            porto3_dir = os.path.dirname(proto3)
+            base_dir = os.path.dirname(porto3_dir)
+            gen_proto3_code(protoc_path, proto3, base_dir, base_dir, base_dir)
+            pb3_files = glob.glob(os.path.join(porto3_dir, f"{proto_base}.proto3.*"))
+            for pb3_file in pb3_files:
+                print(f"Removing {pb3_file}")
+                os.remove(pb3_file)
+
+        if need_rename:
+            if do_onnx_ml:
+                proto_header = qualify(f"{stem}-ml.pb.h", pardir=output)
+            else:
+                proto_header = qualify(f"{stem}.pb.h", pardir=output)
+            print(f"Writing {proto_header}")
+            with open(proto_header, "w", newline="", encoding="utf-8") as fout:
+                fout.write("#pragma once\n")
+                fout.write(f'#include "{proto_base}.pb.h"\n')
+
+    # Generate py mapping
+    # "-" is invalid in python module name, replaces '-' with '_'
+    pb_py = qualify(f"{stem.replace('-', '_')}_pb.py", pardir=output)
+    if need_rename:
+        pb2_py = qualify(f"{proto_base.replace('-', '_')}_pb2.py", pardir=output)
+    else:  # noqa: PLR5501
+        if do_onnx_ml:
+            pb2_py = qualify(f"{stem.replace('-', '_')}_ml_pb2.py", pardir=output)
+        else:
+            pb2_py = qualify(f"{stem.replace('-', '_')}_pb2.py", pardir=output)
+
+    print(f"generating {pb_py}")
+    with open(pb_py, "w", encoding="utf-8") as f:
+        f.write(
+            dedent(
+                f"""\
+                # This file is generated by setup.py. DO NOT EDIT!
+
+
+                from .{os.path.splitext(os.path.basename(pb2_py))[0]} import *  # noqa
+                """
+            )
+        )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Generates .proto file variations from .in.proto"
+    )
+    parser.add_argument(
+        "-p",
+        "--package",
+        default="onnx",
+        help="package name in the generated proto files (default: %(default)s)",
+    )
+    parser.add_argument("-m", "--ml", action="store_true", help="ML mode")
+    parser.add_argument(
+        "-l",
+        "--lite",
+        action="store_true",
+        help="generate lite proto to use with protobuf-lite",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        default=os.path.realpath(os.path.dirname(__file__)),
+        help="output directory (default: %(default)s)",
+    )
+    parser.add_argument(
+        "--protoc_path", default="", help="path to protoc for proto3 file validation"
+    )
+    parser.add_argument(
+        "stems",
+        nargs="*",
+        default=["onnx", "onnx-operators", "onnx-data"],
+        help="list of .in.proto file stems (default: %(default)s)",
+    )
+    args = parser.parse_args()
+
+    if not os.path.exists(args.output):
+        os.makedirs(args.output)
+
+    for stem in args.stems:
+        convert(
+            stem,
+            package_name=args.package,
+            output=args.output,
+            do_onnx_ml=args.ml,
+            lite=args.lite,
+            protoc_path=args.protoc_path,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/helper.py b/pythonProject/.venv/Lib/site-packages/onnx/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cfc5307e133aa84b4289e987e79f2d10c85bd9f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/helper.py
@@ -0,0 +1,1686 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import collections.abc
+import functools
+import math
+import numbers
+import struct
+import typing
+from cmath import isnan
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
+
+import google.protobuf.message
+import numpy as np
+import numpy.typing as npt
+import typing_extensions
+
+import onnx
+from onnx import _mapping, defs, subbyte
+from onnx.onnx_data_pb import MapProto, OptionalProto, SequenceProto
+from onnx.onnx_pb import (
+    AttributeProto,
+    FunctionProto,
+    GraphProto,
+    ModelProto,
+    NodeProto,
+    OperatorSetIdProto,
+    TensorProto,
+    TensorShapeProto,
+    TrainingInfoProto,
+    TypeProto,
+    ValueInfoProto,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import KeysView, Sequence
+
+    from google.protobuf.internal.containers import RepeatedCompositeFieldContainer
+
+VersionRowType = Union[tuple[str, int, int, int], tuple[str, int, int, int, int]]
+VersionTableType = list[VersionRowType]
+AssignmentBindingType = list[tuple[str, str]]
+
+# This is a copy of the documented version in https://github.com/onnx/onnx/blob/main/docs/Versioning.md#released-versions
+# Both must be updated whenever a new version of ONNX is released.
+VERSION_TABLE: VersionTableType = [
+    # Release-version, IR version, ai.onnx version, ai.onnx.ml version, (optional) ai.onnx.training version
+    ("1.0", 3, 1, 1),
+    ("1.1", 3, 5, 1),
+    ("1.1.2", 3, 6, 1),
+    ("1.2", 3, 7, 1),
+    ("1.3", 3, 8, 1),
+    ("1.4.1", 4, 9, 1),
+    ("1.5.0", 5, 10, 1),
+    ("1.6.0", 6, 11, 2),
+    ("1.7.0", 7, 12, 2, 1),
+    ("1.8.0", 7, 13, 2, 1),
+    ("1.8.1", 7, 13, 2, 1),
+    ("1.9.0", 7, 14, 2, 1),
+    ("1.10.0", 8, 15, 2, 1),
+    ("1.10.1", 8, 15, 2, 1),
+    ("1.10.2", 8, 15, 2, 1),
+    ("1.11.0", 8, 16, 3, 1),
+    ("1.12.0", 8, 17, 3, 1),
+    ("1.13.0", 8, 18, 3, 1),
+    ("1.13.1", 8, 18, 3, 1),
+    ("1.14.0", 9, 19, 3, 1),
+    ("1.14.1", 9, 19, 3, 1),
+    ("1.15.0", 9, 20, 4, 1),
+    ("1.16.0", 10, 21, 5, 1),
+    ("1.17.0", 10, 22, 5, 1),
+    ("1.18.0", 11, 23, 5, 1),
+    ("1.19.0", 12, 24, 5, 1),
+]
+
+VersionMapType = dict[tuple[str, int], int]
+
+
+def _create_op_set_id_version_map(table: VersionTableType) -> VersionMapType:
+    """Create a map from (opset-domain, opset-version) to ir-version from above table."""
+    result: VersionMapType = {}
+
+    def process(release_version: str, ir_version: int, *args: Any) -> None:
+        del release_version  # Unused
+        for pair in zip(["ai.onnx", "ai.onnx.ml", "ai.onnx.training"], args):
+            if pair not in result:
+                result[pair] = ir_version
+                if pair[0] == "ai.onnx.training":
+                    result["ai.onnx.preview.training", pair[1]] = ir_version
+
+    for row in table:
+        process(*row)
+    return result
+
+
+OP_SET_ID_VERSION_MAP = _create_op_set_id_version_map(VERSION_TABLE)
+
+
+def find_min_ir_version_for(
+    opsetidlist: Sequence[OperatorSetIdProto], ignore_unknown: bool = False
+) -> int:
+    """Given list of opset ids, determine minimum IR version required.
+
+    Args:
+        opsetidlist: A sequence of OperatorSetIdProto.
+        ignore_unknown: If True, ignore unknown domain and return default minimum
+            version for that domain.
+
+    Returns:
+        The minimum IR version required (integer)
+    """
+    default_min_version = 3
+
+    def find_min(domain: str | None, version: int) -> int:
+        key = (domain or "ai.onnx", version)
+        if key in OP_SET_ID_VERSION_MAP:
+            return OP_SET_ID_VERSION_MAP[key]
+        if ignore_unknown:
+            return default_min_version
+        raise ValueError("Unsupported opset-version.")
+
+    if opsetidlist:
+        return max(find_min(x.domain, x.version) for x in opsetidlist)
+    return default_min_version  # if no opsets specified
+
+
+def make_node(
+    op_type: str,
+    inputs: Sequence[str],
+    outputs: Sequence[str],
+    name: str | None = None,
+    doc_string: str | None = None,
+    domain: str | None = None,
+    overload: str | None = None,
+    **kwargs: Any,
+) -> NodeProto:
+    """Construct a NodeProto.
+
+    Args:
+        op_type (string): The name of the operator to construct
+        inputs (list of string): list of input names
+        outputs (list of string): list of output names
+        name (string, default None): optional unique identifier for NodeProto
+        doc_string (string, default None): optional documentation string for NodeProto
+        domain (string, default None): optional domain for NodeProto.
+            If it's None, we will just use default domain (which is empty)
+        overload (string, default None): optional field, used to
+            resolve calls to model-local functions
+        **kwargs (dict): the attributes of the node.  The acceptable values
+            are documented in :func:`make_attribute`.
+
+    Returns:
+        NodeProto
+    """
+    node = NodeProto()
+    node.op_type = op_type
+    node.input.extend(inputs)
+    node.output.extend(outputs)
+    if name:
+        node.name = name
+    if doc_string:
+        node.doc_string = doc_string
+    if domain is not None:
+        node.domain = domain
+    if overload is not None:
+        node.overload = overload
+    if kwargs:
+        node.attribute.extend(
+            make_attribute(key, value)
+            for key, value in sorted(kwargs.items())
+            if value is not None
+        )
+    return node
+
+
+def make_operatorsetid(
+    domain: str,
+    version: int,
+) -> OperatorSetIdProto:
+    """Construct an OperatorSetIdProto.
+
+    Args:
+        domain (string): The domain of the operator set id
+        version (integer): Version of operator set id
+    Returns:
+        OperatorSetIdProto
+    """
+    operatorsetid = OperatorSetIdProto()
+    operatorsetid.domain = domain
+    operatorsetid.version = version
+    return operatorsetid
+
+
+def make_graph(
+    nodes: Sequence[NodeProto],
+    name: str,
+    inputs: Sequence[ValueInfoProto],
+    outputs: Sequence[ValueInfoProto],
+    initializer: Sequence[TensorProto] | None = None,
+    doc_string: str | None = None,
+    value_info: Sequence[ValueInfoProto] | None = None,
+    sparse_initializer: Sequence[onnx.SparseTensorProto] | None = None,
+) -> GraphProto:
+    """Construct a GraphProto
+
+    Args:
+        nodes: list of NodeProto
+        name (string): graph name
+        inputs: list of ValueInfoProto
+        outputs: list of ValueInfoProto
+        initializer: list of TensorProto
+        doc_string (string): graph documentation
+        value_info: list of ValueInfoProto
+        sparse_initializer: list of onnx.SparseTensorProto
+    Returns:
+        GraphProto
+    """
+    if initializer is None:
+        initializer = []
+    if sparse_initializer is None:
+        sparse_initializer = []
+    if value_info is None:
+        value_info = []
+    graph = GraphProto()
+    graph.node.extend(nodes)
+    graph.name = name
+    graph.input.extend(inputs)
+    graph.output.extend(outputs)
+    graph.initializer.extend(initializer)
+    graph.sparse_initializer.extend(sparse_initializer)
+    graph.value_info.extend(value_info)
+    if doc_string:
+        graph.doc_string = doc_string
+    return graph
+
+
+def make_opsetid(domain: str, version: int) -> OperatorSetIdProto:
+    """Construct an OperatorSetIdProto.
+
+    Args:
+        domain (string): The domain of the operator set id
+        version (integer): Version of operator set id
+    Returns:
+        OperatorSetIdProto
+    """
+    opsetid = OperatorSetIdProto()
+    opsetid.domain = domain
+    opsetid.version = version
+    return opsetid
+
+
+def make_function(
+    domain: str,
+    fname: str,
+    inputs: Sequence[str],
+    outputs: Sequence[str],
+    nodes: Sequence[NodeProto],
+    opset_imports: Sequence[OperatorSetIdProto],
+    attributes: Sequence[str] | None = None,
+    attribute_protos: Sequence[AttributeProto] | None = None,
+    doc_string: str | None = None,
+    overload: str | None = None,
+    value_info: Sequence[ValueInfoProto] | None = None,
+) -> FunctionProto:
+    if attributes is None:
+        attributes = []
+    if attribute_protos is None:
+        attribute_protos = []
+    if value_info is None:
+        value_info = []
+    f = FunctionProto()
+    f.domain = domain
+    f.name = fname
+    f.input.extend(inputs)
+    f.output.extend(outputs)
+    f.node.extend(nodes)
+    f.opset_import.extend(opset_imports)
+    f.attribute.extend(attributes)
+    f.attribute_proto.extend(attribute_protos)
+    if doc_string:
+        f.doc_string = doc_string
+    if overload is not None:
+        f.overload = overload
+    f.value_info.extend(value_info)
+    return f
+
+
+def make_model(graph: GraphProto, **kwargs: Any) -> ModelProto:
+    """Construct a ModelProto
+
+    Args:
+        graph (GraphProto): *make_graph* returns
+        **kwargs: any attribute to add to the returned instance
+    Returns:
+        ModelProto
+    """
+    model = ModelProto()
+    # Touch model.ir_version so it is stored as the version from which it is
+    # generated.
+    model.ir_version = onnx.IR_VERSION
+    model.graph.CopyFrom(graph)
+
+    opset_imports: Sequence[OperatorSetIdProto] | None = kwargs.pop(
+        "opset_imports", None
+    )
+    if opset_imports is not None:
+        model.opset_import.extend(opset_imports)
+    else:
+        # Default import
+        imp = model.opset_import.add()
+        imp.version = defs.onnx_opset_version()
+
+    functions: Sequence[FunctionProto] | None = kwargs.pop("functions", None)
+    if functions is not None:
+        model.functions.extend(functions)
+
+    for k, v in kwargs.items():
+        # TODO: Does this work with repeated fields?
+        setattr(model, k, v)
+    return model
+
+
+# An extension of make_model that infers an IR_VERSION for the model,
+# if not specified, using a best-effort-basis.
+def make_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto:
+    ir_version_field = "ir_version"
+    if ir_version_field not in kwargs:
+        opset_imports_field = "opset_imports"
+        imports = kwargs.get(opset_imports_field, [])
+        kwargs[ir_version_field] = find_min_ir_version_for(imports)
+    return make_model(graph, **kwargs)
+
+
+def set_metadata_props(
+    proto: (
+        ModelProto
+        | GraphProto
+        | FunctionProto
+        | NodeProto
+        | TensorProto
+        | ValueInfoProto
+    ),
+    dict_value: dict[str, str],
+) -> None:
+    del proto.metadata_props[:]
+    for k, v in dict_value.items():
+        entry = proto.metadata_props.add()
+        entry.key = k
+        entry.value = v
+
+
+def set_model_props(model: ModelProto, dict_value: dict[str, str]) -> None:
+    set_metadata_props(model, dict_value)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion"
+)
+def float32_to_bfloat16(fval: float, truncate: bool = False) -> int:
+    # convert a float32 value to a bfloat16 (as int)
+    # By default, this conversion rounds-to-nearest-even and supports NaN
+    # Setting `truncate` to True enables a simpler conversion. In this mode the
+    # conversion is performed by simply dropping the 2 least significant bytes of
+    # the significand. In this mode an error of up to 1 bit may be introduced and
+    # preservation of NaN values is not be guaranteed.
+    ival = int.from_bytes(struct.pack("> 16
+    # NaN requires at least 1 significand bit set
+    if isnan(fval):
+        return 0x7FC0  # sign=0, exp=all-ones, sig=0b1000000
+    # drop bottom 16-bits
+    # round remaining bits using round-to-nearest-even
+    rounded = ((ival >> 16) & 1) + 0x7FFF
+    return (ival + rounded) >> 16
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion"
+)
+def float32_to_float8e4m3(  # noqa: PLR0911
+    fval: float,
+    scale: float = 1.0,
+    fn: bool = True,
+    uz: bool = False,
+    saturate: bool = True,
+) -> int:
+    """Convert a float32 value to a float8, e4m3 (as int).
+
+    See :ref:`onnx-detail-float8` for technical details.
+
+    Args:
+        fval: float to convert
+        scale: scale, divide *fval* by *scale* before casting it
+        fn: no infinite values
+        uz: no negative zero
+        saturate: if True, any value out of range included inf becomes
+            the maximum value, otherwise, it becomes NaN. The
+            description of operator Cast fully describes the
+            differences.
+
+    Returns:
+        converted float
+    """
+    if not fn:
+        raise NotImplementedError(
+            "float32_to_float8e4m3 not implemented with fn=False."
+        )
+    x = fval / scale
+    b = int.from_bytes(struct.pack("> 24  # sign
+    if uz:
+        if (b & 0x7FC00000) == 0x7FC00000:  # noqa: PLR2004
+            return 0x80
+        if np.isinf(x):
+            if saturate:
+                return ret | 127
+            return 0x80
+        e = (b & 0x7F800000) >> 23  # exponent
+        m = b & 0x007FFFFF  # mantissa
+
+        if e < 116:  # noqa: PLR2004
+            ret = 0
+        elif e < 120:  # noqa: PLR2004
+            # denormalized number
+            ex = e - 119
+            if ex >= -2:  # noqa: PLR2004
+                ret |= 1 << (2 + ex)
+                ret |= m >> (21 - ex)
+            elif m > 0:
+                ret |= 1
+            else:
+                ret = 0
+            mask = 1 << (20 - ex)
+            if m & mask and (
+                ret & 1
+                or m & (mask - 1) > 0
+                or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
+            ):
+                # rounding
+                ret += 1
+        elif e < 135:  # noqa: PLR2004
+            # normalized number
+            ex = e - 119  # 127 - 8
+            if ex == 0:
+                ret |= 0x4
+                ret |= m >> 21
+            else:
+                ret |= ex << 3
+                ret |= m >> 20
+            if m & 0x80000 and ((m & 0x100000) or (m & 0x7FFFF)):
+                if (ret & 0x7F) < 0x7F:  # noqa: PLR2004
+                    # rounding
+                    ret += 1
+                elif not saturate:
+                    return 0x80
+        elif saturate:
+            ret |= 0x7F  # 01111110
+        else:
+            ret = 0x80
+        return int(ret)
+    else:
+        if (b & 0x7FC00000) == 0x7FC00000:  # noqa: PLR2004
+            return 0x7F | ret
+        if np.isinf(x):
+            if saturate:
+                return ret | 126
+            return 0x7F | ret
+        e = (b & 0x7F800000) >> 23  # exponent
+        m = b & 0x007FFFFF  # mantissa
+
+        if e != 0:
+            if e < 117:  # noqa: PLR2004
+                pass
+            elif e < 121:  # noqa: PLR2004
+                # denormalized number
+                ex = e - 120
+                if ex >= -2:  # noqa: PLR2004
+                    ret |= 1 << (2 + ex)
+                    ret |= m >> (21 - ex)
+                elif m > 0:
+                    ret |= 1
+                mask = 1 << (20 - ex)
+                if m & mask and (
+                    ret & 1
+                    or m & (mask - 1) > 0
+                    or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
+                ):
+                    # rounding
+                    ret += 1
+            elif e < 136:  # noqa: PLR2004
+                # normalized number
+                ex = e - 120
+                if ex == 0:
+                    ret |= 0x4
+                    ret |= m >> 21
+                else:
+                    ret |= ex << 3
+                    ret |= m >> 20
+                    if (ret & 0x7F) == 0x7F:  # noqa: PLR2004
+                        ret &= 0xFE
+                if (m & 0x80000) and ((m & 0x100000) or (m & 0x7FFFF)):
+                    if (ret & 0x7F) < 0x7E:  # noqa: PLR2004
+                        # rounding
+                        ret += 1
+                    elif not saturate:
+                        ret |= 0x7F
+            elif saturate:
+                ret |= 126  # 01111110
+            else:
+                ret |= 0x7F
+        return int(ret)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion"
+)
+def float32_to_float8e5m2(  # noqa: PLR0911
+    fval: float,
+    scale: float = 1.0,
+    fn: bool = False,
+    uz: bool = False,
+    saturate: bool = True,
+) -> int:
+    """Convert a float32 value to a float8, e5m2 (as int).
+
+    Args:
+        fval: float to convert
+        scale: scale, divide *fval* by *scale* before casting it
+        fn: no infinite values
+        uz: no negative zero
+        saturate: if True, any value out of range included inf becomes
+            the maximum value, otherwise, it becomes NaN. The
+            description of operator Cast fully describes the
+            differences.
+
+    Returns:
+        converted float
+    """
+    x = fval / scale
+    b = int.from_bytes(struct.pack("> 24  # sign
+
+    if fn and uz:
+        if (b & 0x7FC00000) == 0x7FC00000:  # noqa: PLR2004
+            return 0x80
+        if (b & 0x7FFFFFFF) == 0x7F800000:  # noqa: PLR2004
+            # inf
+            if saturate:
+                return ret | 0x7F
+            return 0x80
+        e = (b & 0x7F800000) >> 23  # exponent
+        m = b & 0x007FFFFF  # mantissa
+
+        if e < 109:  # noqa: PLR2004
+            ret = 0
+        elif e < 112:  # noqa: PLR2004
+            # denormalized number
+            ex = e - 111
+            if ex >= -1:
+                ret |= 1 << (1 + ex)
+                ret |= m >> (22 - ex)
+            elif m > 0:
+                ret |= 1
+            else:
+                ret = 0
+            mask = 1 << (21 - ex)
+            if m & mask and (
+                ret & 1
+                or m & (mask - 1) > 0
+                or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
+            ):
+                # rounding
+                ret += 1
+        elif e < 143:  # noqa: PLR2004
+            # normalized number
+            ex = e - 111
+            ret |= ex << 2
+            ret |= m >> 21
+            if m & 0x100000 and ((m & 0xFFFFF) or (m & 0x200000)):
+                if (ret & 0x7F) < 0x7F:  # noqa: PLR2004
+                    # rounding
+                    ret += 1
+                elif not saturate:
+                    ret = 0x80
+        elif e == 255 and m == 0:  # inf  # noqa: PLR2004
+            ret = 0x80
+        elif saturate:
+            ret |= 0x7F  # last possible number
+        else:
+            ret = 0x80
+        return int(ret)
+    elif not fn and not uz:
+        if (b & 0x7FC00000) == 0x7FC00000:  # noqa: PLR2004
+            return 0x7F | ret
+        if np.isinf(x):
+            if saturate:
+                return 0x7B | ret
+            return 0x7C | ret
+        e = (b & 0x7F800000) >> 23  # exponent
+        m = b & 0x007FFFFF  # mantissa
+
+        if e != 0:
+            if e < 110:  # noqa: PLR2004
+                pass
+            elif e < 113:  # noqa: PLR2004
+                # denormalized number
+                ex = e - 112
+                if ex >= -1:
+                    ret |= 1 << (1 + ex)
+                    ret |= m >> (22 - ex)
+                elif m > 0:
+                    ret |= 1
+                mask = 1 << (21 - ex)
+                if m & mask and (
+                    ret & 1
+                    or m & (mask - 1) > 0
+                    or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
+                ):
+                    # rounding
+                    ret += 1
+            elif e < 143:  # noqa: PLR2004
+                # normalized number
+                ex = e - 112
+                ret |= ex << 2
+                ret |= m >> 21
+                if m & 0x100000 and ((m & 0xFFFFF) or (m & 0x200000)):
+                    if (ret & 0x7F) < 0x7B:  # noqa: PLR2004
+                        # rounding
+                        ret += 1
+                    elif saturate:
+                        ret |= 0x7B
+                    else:
+                        ret |= 0x7C
+            elif saturate:
+                ret |= 0x7B
+            else:
+                ret |= 0x7C
+        return int(ret)
+    else:
+        raise NotImplementedError("fn and uz must be both False or True.")
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion"
+)
+def pack_float32_to_4bit(array: np.ndarray | Sequence, signed: bool) -> np.ndarray:
+    """Convert an array of float32 value to a 4bit data-type and pack every two concecutive elements in a byte.
+    See :ref:`onnx-detail-int4` for technical details.
+
+    Args:
+        array: array of float to convert and pack
+        signed: Whether the 4 bit variant is signed or unsigned
+
+    Returns:
+        Packed array with size `ceil(farray.size/2)` (single dimension).
+    """
+    if not isinstance(array, np.ndarray):
+        array = np.asarray(array, dtype=np.float32)
+
+    array_flat: np.ndarray = array.ravel()
+    is_odd_volume = np.prod(array.shape) % 2 == 1
+    if is_odd_volume:
+        array_flat = np.append(array_flat, np.array([0]))
+
+    def single_func(x, y) -> np.ndarray:
+        return subbyte.float32x2_to_4bitx2(x, y, signed)
+
+    func = np.frompyfunc(single_func, 2, 1)
+
+    arr: np.ndarray = func(array_flat[0::2], array_flat[1::2])
+    return arr.astype(np.uint8)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion"
+)
+def pack_float32_to_float4e2m1(array: np.ndarray | Sequence) -> np.ndarray:
+    """Convert an array of float32 value to float4e2m1 and pack every two concecutive elements in a byte.
+    See :ref:`onnx-detail-float4` for technical details.
+
+    Args:
+        array: array of float to convert and pack
+
+    Returns:
+        Packed array of float4e2m1 (as uint8) with size `ceil(farray.size/2)` (single dimension).
+    """
+    if not isinstance(array, np.ndarray):
+        array = np.asarray(array, dtype=np.float32)
+
+    array_flat: np.ndarray = array.ravel()
+    is_odd_volume = np.prod(array.shape) % 2 == 1
+    if is_odd_volume:
+        array_flat = np.append(array_flat, np.array([0]))
+
+    arr = subbyte.float32x2_to_float4e2m1x2(array_flat[0::2], array_flat[1::2])
+    return arr.astype(np.uint8)
+
+
+def _pack_4bitx2(array: np.ndarray) -> npt.NDArray[np.uint8]:
+    """Convert a numpy array to flatten, packed int4/uint4. Elements must be in the correct range."""
+    # Create a 1D copy
+    array_flat = array.ravel().view(np.uint8).copy()
+    size = array.size
+    odd_sized = size % 2 == 1
+    if odd_sized:
+        array_flat.resize([size + 1], refcheck=False)
+    array_flat &= 0x0F
+    array_flat[1::2] <<= 4
+    return array_flat[0::2] | array_flat[1::2]  # type: ignore[return-type]
+
+
+def make_tensor(
+    name: str,
+    data_type: int,
+    dims: Sequence[int],
+    vals: Sequence[int | float] | bytes | np.ndarray,
+    raw: bool = False,
+) -> TensorProto:
+    """Make a TensorProto with specified arguments.  If raw is False, this
+    function will choose the corresponding proto field to store the
+    values based on data_type. If raw is True, use "raw_data" proto
+    field to store the values, and values should be of type bytes in
+    this case.
+
+    Args:
+        name: tensor name
+        data_type: a value such as onnx.TensorProto.FLOAT
+        dims: shape
+        vals: values
+        raw: if True, vals contains the serialized content of the tensor,
+            otherwise, vals should be a list of values of the type defined by ``data_type``.
+
+    Returns:
+        TensorProto
+    """
+    tensor = TensorProto()
+    tensor.data_type = data_type
+    tensor.name = name
+    tensor.dims.extend(dims)
+
+    if data_type == TensorProto.STRING and raw:
+        raise TypeError("Can not use raw_data to store string type.")
+
+    np_dtype = tensor_dtype_to_np_dtype(data_type)
+
+    if raw:
+        # NumPy doesn't have INT4/FP4. It is packed in couples to UINT8 buffers.
+        if data_type in {TensorProto.UINT4, TensorProto.INT4, TensorProto.FLOAT4E2M1}:
+            expected_size_bytes = 0.5
+        else:
+            expected_size_bytes = np_dtype.itemsize
+        expected_size_bytes *= math.prod(dims)
+        expected_size_bytes = math.ceil(expected_size_bytes)
+        if isinstance(vals, np.ndarray):
+            raw_data = vals.tobytes()
+        elif isinstance(vals, bytes):
+            raw_data = vals
+        else:
+            raise TypeError(
+                f"Raw data must be bytes or numpy.ndarray, but got {type(vals)}."
+            )
+        if len(raw_data) != expected_size_bytes:
+            raise ValueError(
+                f"Raw data size does not match tensor's size. Expected {expected_size_bytes} bytes, but got {len(raw_data)} bytes."
+            )
+        tensor.raw_data = raw_data
+        return tensor
+
+    assert not raw, "Bug: raw should be False at this point."
+
+    if data_type == TensorProto.STRING:
+        vals = np.array(vals).flatten()
+        if len(vals) != 0:
+            vals = np.vectorize(_to_bytes)(vals)  # Convert to bytes
+    elif data_type in {
+        TensorProto.FLOAT8E4M3FN,
+        TensorProto.FLOAT8E4M3FNUZ,
+        TensorProto.FLOAT8E5M2,
+        TensorProto.FLOAT8E5M2FNUZ,
+    }:
+        # Float8 values are by default casted using saturating cast.
+        vals = onnx.numpy_helper.saturate_cast(np.asarray(vals), np_dtype).flatten()
+    elif data_type == TensorProto.FLOAT8E8M0:
+        vals = onnx.numpy_helper.to_float8e8m0(
+            np.asarray(vals), saturate=True, round_mode="up"
+        ).flatten()
+    else:
+        vals = np.asarray(vals, dtype=np_dtype).flatten()
+
+    if data_type == TensorProto.COMPLEX128:
+        vals = vals.view(np.float64)  # type: ignore[union-attr]
+    elif data_type == TensorProto.COMPLEX64:
+        vals = vals.view(np.float32)  # type: ignore[union-attr]
+    elif data_type in {TensorProto.BFLOAT16, TensorProto.FLOAT16}:
+        vals = vals.view(np.uint16)  # type: ignore[union-attr]
+    elif data_type in {
+        TensorProto.FLOAT8E4M3FN,
+        TensorProto.FLOAT8E4M3FNUZ,
+        TensorProto.FLOAT8E5M2,
+        TensorProto.FLOAT8E5M2FNUZ,
+        TensorProto.FLOAT8E8M0,
+    }:
+        vals = vals.view(np.uint8)  # type: ignore[union-attr]
+    elif data_type in {TensorProto.UINT4, TensorProto.INT4, TensorProto.FLOAT4E2M1}:
+        # Convert to packed 4-bit representation
+        vals = _pack_4bitx2(vals)  # type: ignore[union-attr,arg-type]
+    elif data_type == TensorProto.BOOL:
+        vals = vals.astype(np.uint8)  # type: ignore[union-attr]
+
+    field = tensor_dtype_to_field(data_type)
+    getattr(tensor, field).extend(vals)
+    return tensor
+
+
+def make_sparse_tensor(
+    values: TensorProto, indices: TensorProto, dims: Sequence[int]
+) -> onnx.SparseTensorProto:
+    """Construct a SparseTensorProto
+
+    Args:
+        values (TensorProto): the values
+        indices (TensorProto): the indices
+        dims: the shape
+
+    Returns:
+        SparseTensorProto
+    """
+    sparse = onnx.SparseTensorProto()
+    sparse.values.CopyFrom(values)
+    sparse.indices.CopyFrom(indices)
+    sparse.dims.extend(dims)
+    return sparse
+
+
+def make_sequence(
+    name: str,
+    elem_type: SequenceProto.DataType,
+    values: Sequence[Any],
+) -> SequenceProto:
+    """Make a Sequence with specified value arguments."""
+    sequence = SequenceProto()
+    sequence.name = name
+    sequence.elem_type = elem_type
+
+    if elem_type == SequenceProto.UNDEFINED:
+        return sequence
+
+    attribute: RepeatedCompositeFieldContainer | None = None
+    if elem_type == SequenceProto.TENSOR:
+        attribute = sequence.tensor_values
+    elif elem_type == SequenceProto.SPARSE_TENSOR:
+        attribute = sequence.sparse_tensor_values
+    elif elem_type == SequenceProto.SEQUENCE:
+        attribute = sequence.sequence_values
+    elif elem_type == SequenceProto.MAP:
+        attribute = sequence.map_values
+    elif elem_type == OptionalProto.OPTIONAL:
+        attribute = sequence.optional_values
+    else:
+        raise TypeError("The element type in the input sequence is not supported.")
+
+    attribute.extend(values)
+    return sequence
+
+
+def make_map(
+    name: str, key_type: int, keys: list[Any], values: SequenceProto
+) -> MapProto:
+    """Make a Map with specified key-value pair arguments.
+
+    Criteria for conversion:
+    - Keys and Values must have the same number of elements
+    - Every key in keys must be of the same type
+    - Every value in values must be of the same type
+    """
+    map_proto = MapProto()
+    valid_key_int_types = [
+        TensorProto.INT8,
+        TensorProto.INT16,
+        TensorProto.INT32,
+        TensorProto.INT64,
+        TensorProto.UINT8,
+        TensorProto.UINT16,
+        TensorProto.UINT32,
+        TensorProto.UINT64,
+    ]
+    map_proto.name = name
+    map_proto.key_type = key_type
+    if key_type == TensorProto.STRING:
+        map_proto.string_keys.extend(keys)
+    elif key_type in valid_key_int_types:
+        map_proto.keys.extend(keys)
+    map_proto.values.CopyFrom(values)
+    return map_proto
+
+
+def make_optional(
+    name: str,
+    elem_type: OptionalProto.DataType,
+    value: google.protobuf.message.Message | None,
+) -> OptionalProto:
+    """Make an Optional with specified value arguments."""
+    optional = OptionalProto()
+    optional.name = name
+    optional.elem_type = elem_type
+
+    if elem_type == OptionalProto.UNDEFINED:
+        return optional
+    attribute: google.protobuf.message.Message | None = None
+    if elem_type == OptionalProto.TENSOR:
+        attribute = optional.tensor_value
+    elif elem_type == OptionalProto.SPARSE_TENSOR:
+        attribute = optional.sparse_tensor_value
+    elif elem_type == OptionalProto.SEQUENCE:
+        attribute = optional.sequence_value
+    elif elem_type == OptionalProto.MAP:
+        attribute = optional.map_value
+    elif elem_type == OptionalProto.OPTIONAL:
+        attribute = optional.optional_value
+    else:
+        raise TypeError("The element type in the input optional is not supported.")
+
+    assert value is not None
+    attribute.CopyFrom(value)  # type: ignore[arg-type]
+    return optional
+
+
+def _to_bytes(value: str | bytes) -> bytes:
+    """Coerce a string (or bytes) value into UTF-8 bytes."""
+    if isinstance(value, str):
+        return value.encode("utf-8")
+    return value
+
+
+def make_attribute(
+    key: str,
+    value: Any,
+    doc_string: str | None = None,
+    attr_type: int | None = None,
+) -> AttributeProto:
+    """Makes an AttributeProto based on the value type."""
+    attr = AttributeProto()
+    attr.name = key
+    if doc_string:
+        attr.doc_string = doc_string
+
+    # Singular cases
+    if isinstance(value, numbers.Integral):
+        attr.i = int(value)
+        attr.type = AttributeProto.INT
+    elif isinstance(value, numbers.Real):
+        attr.f = float(value)
+        attr.type = AttributeProto.FLOAT
+    elif isinstance(value, (str, bytes)):
+        # Encode strings into utf-8
+        attr.s = _to_bytes(value)
+        attr.type = AttributeProto.STRING
+    elif isinstance(value, TensorProto):
+        attr.t.CopyFrom(value)
+        attr.type = AttributeProto.TENSOR
+    elif isinstance(value, onnx.SparseTensorProto):
+        attr.sparse_tensor.CopyFrom(value)
+        attr.type = AttributeProto.SPARSE_TENSOR
+    elif isinstance(value, GraphProto):
+        attr.g.CopyFrom(value)
+        attr.type = AttributeProto.GRAPH
+    elif isinstance(value, TypeProto):
+        attr.tp.CopyFrom(value)
+        attr.type = AttributeProto.TYPE_PROTO
+    # Iterable cases
+    elif isinstance(value, collections.abc.Iterable):
+        value = list(value)
+        if len(value) == 0 and attr_type is None:
+            raise ValueError(
+                f"Could not infer attribute `{key}` type from empty iterator"
+            )
+        if attr_type is None:
+            types = {type(v) for v in value}
+            for exp_t, exp_enum in (
+                (numbers.Integral, AttributeProto.INTS),
+                (numbers.Real, AttributeProto.FLOATS),
+                ((str, bytes), AttributeProto.STRINGS),
+                (TensorProto, AttributeProto.TENSORS),
+                (onnx.SparseTensorProto, AttributeProto.SPARSE_TENSORS),
+                (GraphProto, AttributeProto.GRAPHS),
+                (TypeProto, AttributeProto.TYPE_PROTOS),
+            ):
+                if all(issubclass(t, exp_t) for t in types):  # type: ignore[arg-type]
+                    attr_type = exp_enum
+                    break
+            if attr_type is None:
+                raise ValueError(
+                    "Could not infer the attribute type from the elements of the passed Iterable value."
+                )
+
+        if attr_type == AttributeProto.INTS:
+            attr.ints.extend(value)
+            attr.type = AttributeProto.INTS
+        elif attr_type == AttributeProto.FLOATS:
+            attr.floats.extend(value)
+            attr.type = AttributeProto.FLOATS
+        elif attr_type == AttributeProto.STRINGS:
+            attr.strings.extend(_to_bytes(v) for v in value)
+            attr.type = AttributeProto.STRINGS
+        elif attr_type == AttributeProto.TENSORS:
+            attr.tensors.extend(value)
+            attr.type = AttributeProto.TENSORS
+        elif attr_type == AttributeProto.SPARSE_TENSORS:
+            attr.sparse_tensors.extend(value)
+            attr.type = AttributeProto.SPARSE_TENSORS
+        elif attr_type == AttributeProto.GRAPHS:
+            attr.graphs.extend(value)
+            attr.type = AttributeProto.GRAPHS
+        elif attr_type == AttributeProto.TYPE_PROTOS:
+            attr.type_protos.extend(value)
+            attr.type = AttributeProto.TYPE_PROTOS
+        else:
+            raise AssertionError()  # Should not reach since `ValueError` must be raised in attr_type checking
+    else:
+        raise TypeError(f"'{value}' is not an accepted attribute value.")
+
+    if attr_type is not None and attr.type != attr_type:
+        raise TypeError(
+            f"Inferred attribute type '{_attr_type_to_str(attr.type)}'({attr.type}) mismatched with specified type '{_attr_type_to_str(attr_type)}'({attr_type})"
+        )
+    return attr
+
+
+def make_attribute_ref(
+    name: str, attr_type: AttributeProto.AttributeType, doc_string: str | None = None
+) -> AttributeProto:
+    """Make an AttributeProto holding a reference to the parent function's attribute of given name and type."""
+    attr = AttributeProto()
+    attr.name = name
+    attr.type = attr_type
+    if doc_string:
+        attr.doc_string = doc_string
+    return attr
+
+
+def get_attribute_value(attr: AttributeProto) -> Any:  # noqa: PLR0911
+    if attr.ref_attr_name:
+        raise ValueError(f"Cannot get value of reference attribute: {attr}")
+    if attr.type == AttributeProto.FLOAT:
+        return attr.f
+    if attr.type == AttributeProto.INT:
+        return attr.i
+    if attr.type == AttributeProto.STRING:
+        return attr.s
+    if attr.type == AttributeProto.TENSOR:
+        return attr.t
+    if attr.type == AttributeProto.SPARSE_TENSOR:
+        return attr.sparse_tensor
+    if attr.type == AttributeProto.GRAPH:
+        return attr.g
+    if attr.type == AttributeProto.TYPE_PROTO:
+        return attr.tp
+    if attr.type == AttributeProto.FLOATS:
+        return list(attr.floats)
+    if attr.type == AttributeProto.INTS:
+        return list(attr.ints)
+    if attr.type == AttributeProto.STRINGS:
+        return list(attr.strings)
+    if attr.type == AttributeProto.TENSORS:
+        return list(attr.tensors)
+    if attr.type == AttributeProto.SPARSE_TENSORS:
+        return list(attr.sparse_tensors)
+    if attr.type == AttributeProto.GRAPHS:
+        return list(attr.graphs)
+    if attr.type == AttributeProto.TYPE_PROTOS:
+        return list(attr.type_protos)
+    if attr.type == AttributeProto.UNDEFINED:
+        return None
+    raise ValueError(f"Unsupported ONNX attribute: {attr}")
+
+
+def get_node_attr_value(node: NodeProto, attr_name: str) -> Any:
+    matching = [x for x in node.attribute if x.name == attr_name]
+    if len(matching) > 1:
+        raise ValueError(f"Node has multiple attributes with name {attr_name}")
+    if len(matching) < 1:
+        raise ValueError(f"Node has no attribute with name {attr_name}")
+    return get_attribute_value(matching[0])
+
+
+def make_empty_tensor_value_info(name: str) -> ValueInfoProto:
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    return value_info_proto
+
+
+def make_tensor_type_proto(
+    elem_type: int,
+    shape: Sequence[str | int | None] | None,
+    shape_denotation: list[str] | None = None,
+) -> TypeProto:
+    """Makes a Tensor TypeProto based on the data type and shape."""
+    type_proto = TypeProto()
+    tensor_type_proto = type_proto.tensor_type
+    tensor_type_proto.elem_type = elem_type
+    tensor_shape_proto = tensor_type_proto.shape
+
+    if shape is not None:
+        # You might think this is a no-op (extending a normal Python
+        # list by [] certainly is), but protobuf lists work a little
+        # differently; if a field is never set, it is omitted from the
+        # resulting protobuf; a list that is explicitly set to be
+        # empty will get an (empty) entry in the protobuf. This
+        # difference is visible to our consumers, so make sure we emit
+        # an empty shape!
+        tensor_shape_proto.dim.extend([])
+
+        if shape_denotation and len(shape_denotation) != len(shape):
+            raise ValueError(
+                "Invalid shape_denotation. Must be of the same length as shape."
+            )
+
+        for i, d in enumerate(shape):
+            dim = tensor_shape_proto.dim.add()
+            if d is None:
+                pass
+            elif isinstance(d, int):
+                dim.dim_value = d
+            elif isinstance(d, str):
+                dim.dim_param = d
+            else:
+                raise ValueError(
+                    f"Invalid item in shape: {d}. Needs to be of int or str."
+                )
+
+            if shape_denotation:
+                dim.denotation = shape_denotation[i]
+
+    return type_proto
+
+
+def make_tensor_value_info(
+    name: str,
+    elem_type: int,
+    shape: Sequence[str | int | None] | None,
+    doc_string: str = "",
+    shape_denotation: list[str] | None = None,
+) -> ValueInfoProto:
+    """Makes a ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    tensor_type_proto = make_tensor_type_proto(elem_type, shape, shape_denotation)
+    value_info_proto.type.CopyFrom(tensor_type_proto)
+    return value_info_proto
+
+
+def make_sparse_tensor_type_proto(
+    elem_type: int,
+    shape: Sequence[str | int | None] | None,
+    shape_denotation: list[str] | None = None,
+) -> TypeProto:
+    """Makes a SparseTensor TypeProto based on the data type and shape."""
+    type_proto = TypeProto()
+    sparse_tensor_type_proto = type_proto.sparse_tensor_type
+    sparse_tensor_type_proto.elem_type = elem_type
+    sparse_tensor_shape_proto = sparse_tensor_type_proto.shape
+
+    if shape is not None:
+        # You might think this is a no-op (extending a normal Python
+        # list by [] certainly is), but protobuf lists work a little
+        # differently; if a field is never set, it is omitted from the
+        # resulting protobuf; a list that is explicitly set to be
+        # empty will get an (empty) entry in the protobuf. This
+        # difference is visible to our consumers, so make sure we emit
+        # an empty shape!
+        sparse_tensor_shape_proto.dim.extend([])
+
+        if shape_denotation and len(shape_denotation) != len(shape):
+            raise ValueError(
+                "Invalid shape_denotation. Must be of the same length as shape."
+            )
+
+        for i, d in enumerate(shape):
+            dim = sparse_tensor_shape_proto.dim.add()
+            if d is None:
+                pass
+            elif isinstance(d, int):
+                dim.dim_value = d
+            elif isinstance(d, str):
+                dim.dim_param = d
+            else:
+                raise ValueError(
+                    f"Invalid item in shape: {d}. Needs to be of int or text."
+                )
+
+            if shape_denotation:
+                dim.denotation = shape_denotation[i]
+
+    return type_proto
+
+
+def make_sparse_tensor_value_info(
+    name: str,
+    elem_type: int,
+    shape: Sequence[str | int | None] | None,
+    doc_string: str = "",
+    shape_denotation: list[str] | None = None,
+) -> ValueInfoProto:
+    """Makes a SparseTensor ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    sparse_tensor_type_proto = make_sparse_tensor_type_proto(
+        elem_type, shape, shape_denotation
+    )
+    value_info_proto.type.sparse_tensor_type.CopyFrom(
+        sparse_tensor_type_proto.sparse_tensor_type
+    )
+    return value_info_proto
+
+
+def make_sequence_type_proto(
+    inner_type_proto: TypeProto,
+) -> TypeProto:
+    """Makes a sequence TypeProto."""
+    type_proto = TypeProto()
+    type_proto.sequence_type.elem_type.CopyFrom(inner_type_proto)
+    return type_proto
+
+
+def make_optional_type_proto(
+    inner_type_proto: TypeProto,
+) -> TypeProto:
+    """Makes an optional TypeProto."""
+    type_proto = TypeProto()
+    type_proto.optional_type.elem_type.CopyFrom(inner_type_proto)
+    return type_proto
+
+
+def make_map_type_proto(
+    key_type: int,
+    value_type: TypeProto,
+) -> TypeProto:
+    """Makes a map TypeProto."""
+    type_proto = TypeProto()
+    type_proto.map_type.key_type = key_type
+    type_proto.map_type.value_type.CopyFrom(value_type)
+    return type_proto
+
+
+def make_value_info(
+    name: str,
+    type_proto: TypeProto,
+    doc_string: str = "",
+) -> ValueInfoProto:
+    """Makes a ValueInfoProto with the given type_proto."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    value_info_proto.type.CopyFrom(type_proto)
+    return value_info_proto
+
+
+def _sanitize_str(s: str | bytes) -> str:
+    if isinstance(s, str):
+        sanitized = s
+    elif isinstance(s, bytes):
+        sanitized = s.decode("utf-8", errors="ignore")
+    else:
+        sanitized = str(s)
+    if len(sanitized) < 64:  # noqa: PLR2004
+        return sanitized
+    return sanitized[:64] + f"...<+len={(len(sanitized) - 64)}>"
+
+
+def make_tensor_sequence_value_info(
+    name: str,
+    elem_type: int,
+    shape: Sequence[str | int | None] | None,
+    doc_string: str = "",
+    elem_shape_denotation: list[str] | None = None,
+) -> ValueInfoProto:
+    """Makes a Sequence[Tensors] ValueInfoProto based on the data type and shape."""
+    value_info_proto = ValueInfoProto()
+    value_info_proto.name = name
+    if doc_string:
+        value_info_proto.doc_string = doc_string
+
+    tensor_type_proto = make_tensor_type_proto(elem_type, shape, elem_shape_denotation)
+    sequence_type_proto = make_sequence_type_proto(tensor_type_proto)
+    value_info_proto.type.sequence_type.CopyFrom(sequence_type_proto.sequence_type)
+
+    return value_info_proto
+
+
+def printable_attribute(
+    attr: AttributeProto, subgraphs: bool = False
+) -> str | tuple[str, list[GraphProto]]:
+    content = []
+    content.append(attr.name)
+    content.append("=")
+
+    def str_float(f: float) -> str:
+        # NB: Different Python versions print different numbers of trailing
+        # decimals, specifying this explicitly keeps it consistent for all
+        # versions
+        return f"{f:.15g}"
+
+    def str_int(i: int) -> str:
+        return str(i)
+
+    _T = TypeVar("_T")
+
+    def str_list(str_elem: Callable[[_T], str], xs: Sequence[_T]) -> str:
+        return "[" + ", ".join(map(str_elem, xs)) + "]"
+
+    # for now, this logic should continue to work as long as we are running on a proto3
+    # implementation. If/when we switch to proto3, we will need to use attr.type
+
+    # To support printing subgraphs, if we find a graph attribute, print out
+    # its name here and pass the graph itself up to the caller for later
+    # printing.
+    graphs = []
+    if attr.HasField("f"):
+        content.append(str_float(attr.f))
+    elif attr.HasField("i"):
+        content.append(str_int(attr.i))
+    elif attr.HasField("s"):
+        # TODO: Bit nervous about Python 2 / Python 3 determinism implications
+        content.append(repr(_sanitize_str(attr.s)))
+    elif attr.HasField("t"):
+        if len(attr.t.dims) > 0:
+            content.append("")
+        else:
+            # special case to print scalars
+            field = tensor_dtype_to_field(attr.t.data_type)
+            content.append(f"")
+    elif attr.HasField("g"):
+        content.append(f"")
+        graphs.append(attr.g)
+    elif attr.HasField("tp"):
+        content.append(f"")
+    elif attr.floats:
+        content.append(str_list(str_float, attr.floats))
+    elif attr.ints:
+        content.append(str_list(str_int, attr.ints))
+    elif attr.strings:
+        # TODO: Bit nervous about Python 2 / Python 3 determinism implications
+        content.append(str(list(map(_sanitize_str, attr.strings))))
+    elif attr.tensors:
+        content.append("[, ...]")
+    elif attr.type_protos:
+        content.append("[")
+        for i, tp in enumerate(attr.type_protos):
+            comma = "," if i != len(attr.type_protos) - 1 else ""
+            content.append(f"{comma}")
+        content.append("]")
+    elif attr.graphs:
+        content.append("[")
+        for i, g in enumerate(attr.graphs):
+            comma = "," if i != len(attr.graphs) - 1 else ""
+            content.append(f"{comma}")
+        content.append("]")
+        graphs.extend(attr.graphs)
+    else:
+        content.append("")
+    if subgraphs:
+        return " ".join(content), graphs
+    return " ".join(content)
+
+
+def printable_dim(dim: TensorShapeProto.Dimension) -> str:
+    which = dim.WhichOneof("value")
+    if which is None:
+        return "?"
+    return str(getattr(dim, which))
+
+
+def printable_type(t: TypeProto) -> str:
+    if t.WhichOneof("value") == "tensor_type":
+        s: str = TensorProto.DataType.Name(t.tensor_type.elem_type)  # type: ignore[attr-defined]
+        if t.tensor_type.HasField("shape"):
+            if len(t.tensor_type.shape.dim):
+                s += str(", " + "x".join(map(printable_dim, t.tensor_type.shape.dim)))
+            else:
+                s += ", scalar"
+        return s
+    if t.WhichOneof("value") is None:
+        return ""
+    return f"Unknown type {t.WhichOneof('value')}"
+
+
+def printable_value_info(v: ValueInfoProto) -> str:
+    s = f"%{v.name}"
+    if v.type:
+        s = f"{s}[{printable_type(v.type)}]"
+    return s
+
+
+def printable_tensor_proto(t: TensorProto) -> str:
+    s = f"%{t.name}["
+    s += TensorProto.DataType.Name(t.data_type)  # type: ignore[attr-defined]
+    if t.dims is not None:
+        if len(t.dims):
+            s += str(", " + "x".join(map(str, t.dims)))
+        else:
+            s += ", scalar"
+    s += "]"
+    return s
+
+
+def printable_node(
+    node: NodeProto, prefix: str = "", subgraphs: bool = False
+) -> str | tuple[str, list[GraphProto]]:
+    content = []
+    if len(node.output):
+        content.append(", ".join([f"%{name}" for name in node.output]))
+        content.append("=")
+    # To deal with nested graphs
+    graphs: list[GraphProto] = []
+    printed_attrs = []
+    for attr in node.attribute:
+        if subgraphs:
+            printed_attr_subgraphs = printable_attribute(attr, subgraphs)
+            if not isinstance(printed_attr_subgraphs[1], list):
+                raise TypeError(
+                    f"printed_attr_subgraphs[1] must be an instance of {list}."
+                )
+            graphs.extend(printed_attr_subgraphs[1])
+            printed_attrs.append(printed_attr_subgraphs[0])
+        else:
+            printed = printable_attribute(attr)
+            if not isinstance(printed, str):
+                raise TypeError(f"printed must be an instance of {str}.")
+            printed_attrs.append(printed)
+    printed_attributes = ", ".join(sorted(printed_attrs))
+    printed_inputs = ", ".join([f"%{name}" for name in node.input])
+    if node.attribute:
+        content.append(f"{node.op_type}[{printed_attributes}]({printed_inputs})")
+    else:
+        content.append(f"{node.op_type}({printed_inputs})")
+    if subgraphs:
+        return prefix + " ".join(content), graphs
+    return prefix + " ".join(content)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.19. Consider using onnx.printer.to_text() instead."
+)
+def printable_graph(graph: GraphProto, prefix: str = "") -> str:
+    """Display a GraphProto as a string.
+
+    .. deprecated:: 1.19
+        Consider using :func:`onnx.printer.to_text` instead.
+
+    Args:
+        graph (GraphProto): the graph to display
+        prefix (string): prefix of every line
+
+    Returns:
+        string
+    """
+    content = []
+    indent = prefix + "  "
+    # header
+    header = ["graph", graph.name]
+    initializers = {t.name for t in graph.initializer}
+    if len(graph.input):
+        header.append("(")
+        in_strs = []  # required inputs
+        in_with_init_strs: list = []  # optional inputs with initializer providing default value
+        for inp in graph.input:
+            if inp.name not in initializers:
+                in_strs.append(printable_value_info(inp))
+            else:
+                in_with_init_strs.append(printable_value_info(inp))
+        if in_strs:
+            content.append(prefix + " ".join(header))
+            header = []
+            for line in in_strs:
+                content.append(prefix + "  " + line)  # noqa: PERF401
+        header.append(")")
+
+        if in_with_init_strs:
+            header.append("optional inputs with matching initializers (")
+            content.append(prefix + " ".join(header))
+            header = []
+            for line in in_with_init_strs:
+                content.append(prefix + "  " + line)  # noqa: PERF401
+            header.append(")")
+
+        # from IR 4 onwards an initializer is not required to have a matching graph input
+        # so output the name, type and shape of those as well
+        if len(in_with_init_strs) < len(initializers):
+            graph_inputs = {i.name for i in graph.input}
+            init_strs = [
+                printable_tensor_proto(i)
+                for i in graph.initializer
+                if i.name not in graph_inputs
+            ]
+            header.append("initializers (")
+            content.append(prefix + " ".join(header))
+            header = []
+            for line in init_strs:
+                content.append(prefix + "  " + line)  # noqa: PERF401
+            header.append(")")
+
+    header.append("{")
+    content.append(prefix + " ".join(header))
+    graphs: list[GraphProto] = []
+    # body
+    for node in graph.node:
+        contents_subgraphs = printable_node(node, indent, subgraphs=True)
+        if not isinstance(contents_subgraphs[1], list):
+            raise TypeError(f"contents_subgraphs[1] must be an instance of {list}.")
+        content.append(contents_subgraphs[0])
+        graphs.extend(contents_subgraphs[1])
+    # tail
+    tail = ["return"]
+    if len(graph.output):
+        tail.append(", ".join([f"%{out.name}" for out in graph.output]))
+    content.append(indent + " ".join(tail))
+    # closing bracket
+    content.append(prefix + "}")
+    for g in graphs:
+        content.append("\n" + printable_graph(g))  # noqa: PERF401
+    return "\n".join(content)
+
+
+def strip_doc_string(proto: google.protobuf.message.Message) -> None:
+    """Empties `doc_string` field on any nested protobuf messages"""
+    if not isinstance(proto, google.protobuf.message.Message):
+        raise TypeError(
+            f"proto must be an instance of {google.protobuf.message.Message}."
+        )
+    for descriptor in proto.DESCRIPTOR.fields:
+        if descriptor.name == "doc_string":
+            proto.ClearField(descriptor.name)
+        elif descriptor.type == descriptor.TYPE_MESSAGE:
+            if descriptor.label == descriptor.LABEL_REPEATED:
+                for x in getattr(proto, descriptor.name):
+                    strip_doc_string(x)
+            elif proto.HasField(descriptor.name):
+                strip_doc_string(getattr(proto, descriptor.name))
+
+
+def make_training_info(
+    algorithm: GraphProto,
+    algorithm_bindings: AssignmentBindingType,
+    initialization: GraphProto | None,
+    initialization_bindings: AssignmentBindingType | None,
+) -> TrainingInfoProto:
+    training_info = TrainingInfoProto()
+    training_info.algorithm.CopyFrom(algorithm)
+    for k, v in algorithm_bindings:
+        binding = training_info.update_binding.add()
+        binding.key = k
+        binding.value = v
+
+    if initialization:
+        training_info.initialization.CopyFrom(initialization)
+    if initialization_bindings:
+        for k, v in initialization_bindings:
+            binding = training_info.initialization_binding.add()
+            binding.key = k
+            binding.value = v
+
+    return training_info
+
+
+# Following functions are used for mapping
+def tensor_dtype_to_np_dtype(tensor_dtype: int) -> np.dtype:
+    """Convert a TensorProto's data_type to corresponding numpy dtype. It can be used while making tensor.
+
+    Args:
+        tensor_dtype: TensorProto's data_type
+
+    Returns:
+        numpy's data_type
+    """
+    return _mapping.TENSOR_TYPE_MAP[tensor_dtype].np_dtype
+
+
+def tensor_dtype_to_storage_tensor_dtype(tensor_dtype: int) -> int:
+    """Convert a TensorProto's data_type to corresponding data_type for storage.
+
+    Args:
+        tensor_dtype: TensorProto's data_type
+
+    Returns:
+        data_type for storage
+    """
+    return _mapping.TENSOR_TYPE_MAP[tensor_dtype].storage_dtype
+
+
+def tensor_dtype_to_string(tensor_dtype: int) -> str:
+    """Get the name of given TensorProto's data_type.
+
+    Args:
+        tensor_dtype: TensorProto's data_type
+
+    Returns:
+        the name of data_type
+    """
+    return _mapping.TENSOR_TYPE_MAP[tensor_dtype].name
+
+
+@functools.lru_cache(None)
+def tensor_dtype_to_field(tensor_dtype: int) -> str:
+    """Convert a TensorProto's data_type to corresponding field name for storage. It can be used while making tensors.
+
+    Args:
+        tensor_dtype: TensorProto's data_type
+
+    Returns:
+        field name
+    """
+    storage_tensor_type_to_field = {
+        int(TensorProto.FLOAT): "float_data",
+        int(TensorProto.INT32): "int32_data",
+        int(TensorProto.INT64): "int64_data",
+        int(TensorProto.DOUBLE): "double_data",
+        int(TensorProto.UINT32): "uint64_data",
+        int(TensorProto.UINT64): "uint64_data",
+        int(TensorProto.STRING): "string_data",
+    }
+    return storage_tensor_type_to_field[
+        _mapping.TENSOR_TYPE_MAP[tensor_dtype].storage_dtype
+    ]
+
+
+@functools.lru_cache(None)
+def np_dtype_to_tensor_dtype(np_dtype: np.dtype) -> TensorProto.DataType:
+    """Convert a numpy's dtype to corresponding tensor type. It can be used while converting numpy arrays to tensors.
+
+    Args:
+        np_dtype: numpy's data_type
+
+    Returns:
+        TensorsProto's data_type
+    """
+    _np_dtype_to_tensor_dtype = {
+        v.np_dtype: k for k, v in _mapping.TENSOR_TYPE_MAP.items()
+    }
+    if np_dtype in _np_dtype_to_tensor_dtype:
+        return typing.cast("TensorProto.DataType", _np_dtype_to_tensor_dtype[np_dtype])
+    if np.issubdtype(np_dtype, np.str_):
+        return TensorProto.STRING  # type: ignore[no-any-return]
+
+    raise ValueError(
+        f"Unable to convert type {np_dtype!r} into TensorProto element type."
+    )
+
+
+def get_all_tensor_dtypes() -> KeysView[int]:
+    """Get all tensor types from TensorProto.
+
+    Returns:
+        all tensor types from TensorProto
+    """
+    return _mapping.TENSOR_TYPE_MAP.keys()
+
+
+_ATTRIBUTE_TYPE_TO_STR: dict[int, str] = {
+    k: v
+    for v, k in AttributeProto.AttributeType.items()  # type: ignore[attr-defined]
+}
+
+
+def _attr_type_to_str(attr_type: int) -> str:
+    """Convert AttributeProto type to string.
+
+    Args:
+        attr_type: AttributeProto type.
+
+    Returns:
+        String representing the supplied attr_type.
+    """
+    if attr_type in AttributeProto.AttributeType.values():  # type: ignore[attr-defined]
+        return _ATTRIBUTE_TYPE_TO_STR[attr_type]
+    return AttributeProto.AttributeType.keys()[0]  # type: ignore
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/hub.py b/pythonProject/.venv/Lib/site-packages/onnx/hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..496f107f809a44bc8c21e5ebb2bbe105993de663
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/hub.py
@@ -0,0 +1,483 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+"""ONNX Model Hub
+
+This implements the python client for the ONNX model hub.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import sys
+from io import BytesIO
+from os.path import join
+from typing import IO, Any, cast
+from urllib.error import HTTPError
+from urllib.request import urlopen
+
+import onnx
+
+if "ONNX_HOME" in os.environ:
+    _ONNX_HUB_DIR = join(os.environ["ONNX_HOME"], "hub")
+elif "XDG_CACHE_HOME" in os.environ:
+    _ONNX_HUB_DIR = join(os.environ["XDG_CACHE_HOME"], "onnx", "hub")
+else:
+    _ONNX_HUB_DIR = join(os.path.expanduser("~"), ".cache", "onnx", "hub")
+
+
+class ModelInfo:
+    """A class to represent a model's property and metadata in the ONNX Hub.
+    It extracts model name, path, sha, tags, etc. from the passed in raw_model_info dict.
+
+    Attributes:
+        model: The name of the model.
+        model_path: The path to the model, relative to the model zoo (https://github.com/onnx/models/) repo root.
+        metadata: Additional metadata of the model, such as the size of the model, IO ports, etc.
+        model_sha: The SHA256 digest of the model file.
+        tags: A set of tags associated with the model.
+        opset: The opset version of the model.
+    """
+
+    def __init__(self, raw_model_info: dict[str, Any]) -> None:
+        """Initializer.
+
+        Args:
+            raw_model_info: A JSON dict containing the model info.
+        """
+        self.model = cast("str", raw_model_info["model"])
+
+        self.model_path = cast("str", raw_model_info["model_path"])
+        self.metadata: dict[str, Any] = cast(
+            "dict[str, Any]", raw_model_info["metadata"]
+        )
+        self.model_sha: str | None = None
+        if "model_sha" in self.metadata:
+            self.model_sha = cast("str", self.metadata["model_sha"])
+
+        self.tags: set[str] = set()
+        if "tags" in self.metadata:
+            self.tags = set(cast("list[str]", self.metadata["tags"]))
+
+        self.opset = cast("int", raw_model_info["opset_version"])
+        self.raw_model_info: dict[str, Any] = raw_model_info
+
+    def __str__(self) -> str:
+        return f"ModelInfo(model={self.model}, opset={self.opset}, path={self.model_path}, metadata={self.metadata})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+
+def set_dir(new_dir: str) -> None:
+    """Sets the current ONNX hub cache location.
+
+    Args:
+        new_dir: Location of new model hub cache.
+    """
+    global _ONNX_HUB_DIR  # noqa: PLW0603
+    _ONNX_HUB_DIR = new_dir
+
+
+def get_dir() -> str:
+    """Gets the current ONNX hub cache location.
+
+    Returns:
+        The location of the ONNX hub model cache.
+    """
+    return _ONNX_HUB_DIR
+
+
+def _parse_repo_info(repo: str) -> tuple[str, str, str]:
+    """Gets the repo owner, name and ref from a repo specification string."""
+    repo_owner = repo.split(":")[0].split("/")[0]
+    repo_name = repo.split(":")[0].split("/")[1]
+    if ":" in repo:
+        repo_ref = repo.split(":")[1]
+    else:
+        repo_ref = "main"
+    return repo_owner, repo_name, repo_ref
+
+
+def _verify_repo_ref(repo: str) -> bool:
+    """Verifies whether the given model repo can be trusted.
+    A model repo can be trusted if it matches onnx/models:main.
+    """
+    repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
+    return (repo_owner == "onnx") and (repo_name == "models") and (repo_ref == "main")
+
+
+def _get_base_url(repo: str, lfs: bool = False) -> str:
+    """Gets the base github url from a repo specification string.
+
+    Args:
+        repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main".
+        lfs: Whether the url is for downloading lfs models.
+
+    Returns:
+        The base github url for downloading.
+    """
+    repo_owner, repo_name, repo_ref = _parse_repo_info(repo)
+
+    if lfs:
+        return f"https://media.githubusercontent.com/media/{repo_owner}/{repo_name}/{repo_ref}/"
+    return f"https://raw.githubusercontent.com/{repo_owner}/{repo_name}/{repo_ref}/"
+
+
+def _download_file(url: str, file_name: str) -> None:
+    """Downloads the file with specified file_name from the url.
+
+    Args:
+        url: A url of download link.
+        file_name: A specified file name for the downloaded file.
+    """
+    chunk_size = 16384  # 1024 * 16
+    with urlopen(url) as response, open(file_name, "wb") as f:
+        # Loads processively with chuck_size for huge models
+        while True:
+            chunk = response.read(chunk_size)
+            if not chunk:
+                break
+            f.write(chunk)
+
+
+def list_models(
+    repo: str = "onnx/models:main",
+    model: str | None = None,
+    tags: list[str] | None = None,
+) -> list[ModelInfo]:
+    """Gets the list of model info consistent with a given name and tags
+
+    Args:
+        repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        model: The name of the model to search for. If `None`, will
+            return all models with matching tags.
+        tags: A list of tags to filter models by. If `None`, will return
+            all models with matching name.
+
+    Returns:
+        ``ModelInfo``s.
+    """
+    base_url = _get_base_url(repo)
+    manifest_url = base_url + "ONNX_HUB_MANIFEST.json"
+    try:
+        with urlopen(manifest_url) as response:
+            manifest: list[ModelInfo] = [
+                ModelInfo(info) for info in json.load(cast("IO[str]", response))
+            ]
+    except HTTPError as e:
+        raise AssertionError(f"Could not find manifest at {manifest_url}") from e
+
+    # Filter by model name first.
+    matching_models = (
+        manifest
+        if model is None
+        else [m for m in manifest if m.model.lower() == model.lower()]
+    )
+
+    # Filter by tags
+    if tags is None:
+        return matching_models
+
+    canonical_tags = {t.lower() for t in tags}
+    matching_info_list: list[ModelInfo] = []
+    for m in matching_models:
+        model_tags = {t.lower() for t in m.tags}
+        if len(canonical_tags.intersection(model_tags)) > 0:
+            matching_info_list.append(m)
+    return matching_info_list
+
+
+def get_model_info(
+    model: str, repo: str = "onnx/models:main", opset: int | None = None
+) -> ModelInfo:
+    """Gets the model info matching the given name and opset.
+
+    Args:
+        model: The name of the onnx model in the manifest. This field is
+            case-sensitive
+        repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        opset: The opset of the model to get. The default of `None` will
+            return the model with largest opset.
+
+    Returns:
+        ``ModelInfo``.
+    """
+    matching_models = list_models(repo, model)
+    if not matching_models:
+        raise AssertionError(f"No models found with name {model}")
+
+    if opset is None:
+        selected_models = sorted(matching_models, key=lambda m: -m.opset)
+    else:
+        selected_models = [m for m in matching_models if m.opset == opset]
+        if not selected_models:
+            valid_opsets = [m.opset for m in matching_models]
+            raise AssertionError(
+                f"{model} has no version with opset {opset}. Valid opsets: {valid_opsets}"
+            )
+    return selected_models[0]
+
+
+def load(
+    model: str,
+    repo: str = "onnx/models:main",
+    opset: int | None = None,
+    force_reload: bool = False,
+    silent: bool = False,
+) -> onnx.ModelProto | None:
+    """Downloads a model by name from the onnx model hub.
+
+    Args:
+        model: The name of the onnx model in the manifest. This field is
+            case-sensitive
+        repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        opset: The opset of the model to download. The default of `None`
+            automatically chooses the largest opset
+        force_reload: Whether to force the model to re-download even if
+            its already found in the cache
+        silent: Whether to suppress the warning message if the repo is
+            not trusted.
+
+    Returns:
+        ModelProto or None
+    """
+    selected_model = get_model_info(model, repo, opset)
+    local_model_path_arr = selected_model.model_path.split("/")
+    if selected_model.model_sha is not None:
+        local_model_path_arr[-1] = (
+            f"{selected_model.model_sha}_{local_model_path_arr[-1]}"
+        )
+    local_model_path = join(_ONNX_HUB_DIR, os.sep.join(local_model_path_arr))
+
+    if force_reload or not os.path.exists(local_model_path):
+        if not _verify_repo_ref(repo) and not silent:
+            msg = f"The model repo specification {repo} is not trusted and may contain security vulnerabilities. Only continue if you trust this repo."
+
+            print(msg, file=sys.stderr)
+            print("Continue?[y/n]")
+            if input().lower() != "y":
+                return None
+
+        os.makedirs(os.path.dirname(local_model_path), exist_ok=True)
+        lfs_url = _get_base_url(repo, True)
+        print(f"Downloading {model} to local path {local_model_path}")
+        _download_file(lfs_url + selected_model.model_path, local_model_path)
+    else:
+        print(f"Using cached {model} model from {local_model_path}")
+
+    with open(local_model_path, "rb") as f:
+        model_bytes = f.read()
+
+    if selected_model.model_sha is not None:
+        downloaded_sha = hashlib.sha256(model_bytes).hexdigest()
+        if not downloaded_sha == selected_model.model_sha:
+            raise AssertionError(
+                f"The cached model {selected_model.model} has SHA256 {downloaded_sha} "
+                f"while checksum should be {selected_model.model_sha}. "
+                "The model in the hub may have been updated. Use force_reload to "
+                "download the model from the model hub."
+            )
+
+    return onnx.load(cast("IO[bytes]", BytesIO(model_bytes)))
+
+
+def download_model_with_test_data(
+    model: str,
+    repo: str = "onnx/models:main",
+    opset: int | None = None,
+    force_reload: bool = False,
+    silent: bool = False,
+) -> str | None:
+    """Downloads a model along with test data by name from the onnx model hub and returns the directory to which the files have been extracted.
+    Users are responsible for making sure the model comes from a trusted source, and the data is safe to be extracted.
+
+    Args:
+        model: The name of the onnx model in the manifest. This field is
+            case-sensitive
+        repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        opset: The opset of the model to download. The default of `None`
+            automatically chooses the largest opset
+        force_reload: Whether to force the model to re-download even if
+            its already found in the cache
+        silent: Whether to suppress the warning message if the repo is
+            not trusted.
+
+    Returns:
+        str or None
+    """
+    selected_model = get_model_info(model, repo, opset)
+
+    local_model_with_data_path_arr = selected_model.metadata[
+        "model_with_data_path"
+    ].split("/")
+
+    model_with_data_sha = selected_model.metadata["model_with_data_sha"]
+
+    if model_with_data_sha is not None:
+        local_model_with_data_path_arr[-1] = (
+            f"{model_with_data_sha}_{local_model_with_data_path_arr[-1]}"
+        )
+    local_model_with_data_path = join(
+        _ONNX_HUB_DIR, os.sep.join(local_model_with_data_path_arr)
+    )
+
+    if force_reload or not os.path.exists(local_model_with_data_path):
+        if not _verify_repo_ref(repo) and not silent:
+            msg = f"The model repo specification {repo} is not trusted and may contain security vulnerabilities. Only continue if you trust this repo."
+
+            print(msg, file=sys.stderr)
+            print("Continue?[y/n]")
+            if input().lower() != "y":
+                return None
+
+        os.makedirs(os.path.dirname(local_model_with_data_path), exist_ok=True)
+        lfs_url = _get_base_url(repo, True)
+        print(f"Downloading {model} to local path {local_model_with_data_path}")
+        _download_file(
+            lfs_url + selected_model.metadata["model_with_data_path"],
+            local_model_with_data_path,
+        )
+    else:
+        print(f"Using cached {model} model from {local_model_with_data_path}")
+
+    with open(local_model_with_data_path, "rb") as f:
+        model_with_data_bytes = f.read()
+
+    if model_with_data_sha is not None:
+        downloaded_sha = hashlib.sha256(model_with_data_bytes).hexdigest()
+        if not downloaded_sha == model_with_data_sha:
+            raise AssertionError(
+                f"The cached model {selected_model.model} has SHA256 {downloaded_sha} "
+                f"while checksum should be {model_with_data_sha}. "
+                "The model in the hub may have been updated. Use force_reload to "
+                "download the model from the model hub."
+            )
+
+    local_model_with_data_dir_path = local_model_with_data_path[
+        0 : len(local_model_with_data_path) - len(".tar.gz")
+    ]
+    onnx.utils._extract_model_safe(
+        local_model_with_data_path, local_model_with_data_dir_path
+    )
+    model_with_data_path = (
+        local_model_with_data_dir_path
+        + "/"
+        + os.listdir(local_model_with_data_dir_path)[0]
+    )
+
+    return model_with_data_path
+
+
+def load_composite_model(
+    network_model: str,
+    preprocessing_model: str,
+    network_repo: str = "onnx/models:main",
+    preprocessing_repo: str = "onnx/models:main",
+    opset: int | None = None,
+    force_reload: bool = False,
+    silent: bool = False,
+) -> onnx.ModelProto | None:
+    """Builds a composite model including data preprocessing by downloading a network and a preprocessing model
+    and combine it into a single model
+
+    Args:
+        network_model: The name of the onnx model in the manifest.
+        preprocessing_model: The name of the preprocessing model.
+        network_repo: The location of the model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        preprocessing_repo: The location of the proprocessing model repo in format
+            "user/repo[:branch]". If no branch is found will default to
+            "main"
+        opset: The opset of the model to download. The default of `None`
+            automatically chooses the largest opset
+        force_reload: Whether to force the model to re-download even if
+            its already found in the cache
+        silent: Whether to suppress the warning message if the repo is
+            not trusted.
+
+    Returns:
+        ModelProto or None
+    """
+    preprocessing = load(
+        preprocessing_model, preprocessing_repo, opset, force_reload, silent
+    )
+    if preprocessing is None:
+        raise RuntimeError(
+            f"Could not load the preprocessing model: {preprocessing_model}"
+        )
+    network = load(network_model, network_repo, opset, force_reload, silent)
+    if network is None:
+        raise RuntimeError(f"Could not load the network model: {network_model}")
+
+    all_domains: set[str] = set()
+    domains_to_version_network: dict[str, int] = {}
+    domains_to_version_preprocessing: dict[str, int] = {}
+
+    for opset_import_entry in network.opset_import:
+        domain = (
+            "ai.onnx" if opset_import_entry.domain == "" else opset_import_entry.domain
+        )
+        all_domains.add(domain)
+        domains_to_version_network[domain] = opset_import_entry.version
+
+    for opset_import_entry in preprocessing.opset_import:
+        domain = (
+            "ai.onnx" if opset_import_entry.domain == "" else opset_import_entry.domain
+        )
+        all_domains.add(domain)
+        domains_to_version_preprocessing[domain] = opset_import_entry.version
+
+    preprocessing_opset_version = -1
+    network_opset_version = -1
+    for domain in all_domains:
+        if domain == "ai.onnx":
+            preprocessing_opset_version = domains_to_version_preprocessing[domain]
+            network_opset_version = domains_to_version_network[domain]
+        elif (
+            domain in domains_to_version_preprocessing
+            and domain in domains_to_version_network
+            and domains_to_version_preprocessing[domain]
+            != domains_to_version_preprocessing[domain]
+        ):
+            raise ValueError(
+                f"Can not merge {preprocessing_model} and {network_model} because they contain "
+                f"different opset versions for domain {domain} ({domains_to_version_preprocessing[domain]}) "
+                f"and {domains_to_version_network[domain]}). Only the default domain can be "
+                "automatically converted to the highest version of the two."
+            )
+    if preprocessing_opset_version > network_opset_version:
+        network = onnx.version_converter.convert_version(
+            network, preprocessing_opset_version
+        )
+        network.ir_version = preprocessing.ir_version
+        onnx.checker.check_model(network)
+    elif network_opset_version > preprocessing_opset_version:
+        preprocessing = onnx.version_converter.convert_version(
+            preprocessing, network_opset_version
+        )
+        preprocessing.ir_version = network.ir_version
+        onnx.checker.check_model(preprocessing)
+
+    io_map = [
+        (out_entry.name, in_entry.name)
+        for out_entry, in_entry in zip(preprocessing.graph.output, network.graph.input)
+    ]
+
+    model_with_preprocessing = onnx.compose.merge_models(
+        preprocessing, network, io_map=io_map
+    )
+    return model_with_preprocessing
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/inliner.py b/pythonProject/.venv/Lib/site-packages/onnx/inliner.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb12d76bcae1089c7b16821fc0329a8933cf45b0
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/inliner.py
@@ -0,0 +1,60 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import onnx
+import onnx.onnx_cpp2py_export.inliner as C  # noqa: N812
+
+
+def inline_local_functions(
+    model: onnx.ModelProto, convert_version: bool = False
+) -> onnx.ModelProto:
+    """Inline model-local functions in given model.
+
+    Arguments:
+        model: an ONNX ModelProto
+        convert_version: if true, try to apply automatic version-conversion to functions requiring a
+            different (ONNX) opset version from the model.
+
+    Returns:
+        ModelProto with all calls to model-local functions inlined (recursively)
+    """
+    result = C.inline_local_functions(model.SerializeToString(), convert_version)
+    inlined_model = onnx.ModelProto()
+    inlined_model.ParseFromString(result)
+    return inlined_model
+
+
+def inline_selected_functions(
+    model: onnx.ModelProto,
+    function_ids: list[tuple[str, str]],
+    exclude: bool = False,
+    inline_schema_functions: bool = False,
+) -> onnx.ModelProto:
+    """Inline selected functions in given model.
+
+    Arguments:
+        model: an ONNX ModelProto
+        function_ids: list of functions to include/exclude when inlining. Each
+            element is a tuple of (function domain, function name).
+        exclude: if true, inlines all functions except those specified in function_ids.
+           if false, inlines all functions specified in function_ids.
+        inline_schema_functions: if true, inlines schema-defined functions as well
+            as model-local functions. Otherwise, only model-local functions are inlined.
+
+    Returns:
+        ModelProto with all calls to model-local functions inlined (recursively)
+    """
+    if inline_schema_functions:
+        result = C.inline_selected_functions2(
+            model.SerializeToString(), function_ids, exclude
+        )
+    else:
+        result = C.inline_selected_functions(
+            model.SerializeToString(), function_ids, exclude
+        )
+    inlined_model = onnx.ModelProto()
+    inlined_model.ParseFromString(result)
+    return inlined_model
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/model_container.py b/pythonProject/.venv/Lib/site-packages/onnx/model_container.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a95c9e482d91262fafddacc719f85bc55679628
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/model_container.py
@@ -0,0 +1,350 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+"""Implements function make_large_model to easily create and save models
+bigger than 2 Gb.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+import onnx
+import onnx.external_data_helper as ext_data
+import onnx.helper
+import onnx.onnx_cpp2py_export.checker as c_checker
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+def _set_external_data(
+    tensor: onnx.TensorProto,
+    location: str,
+    offset: int | None = None,
+    length: int | None = None,
+    checksum: str | None = None,
+    basepath: str | None = None,
+) -> None:
+    del tensor.external_data[:]
+    tensor.data_location = onnx.TensorProto.EXTERNAL
+    for k, v in {
+        "location": location,
+        "offset": offset,
+        "length": length,
+        "checksum": checksum,
+        "basepath": basepath,
+    }.items():
+        if v is not None:
+            entry = tensor.external_data.add()
+            entry.key = k
+            entry.value = str(v)
+
+
+def _enumerate_subgraphs(graph):
+    for node in graph.node:
+        for att in node.attribute:
+            if att.g:
+                yield att.g
+                yield from _enumerate_subgraphs(att.g)
+
+
+def make_large_tensor_proto(
+    location: str, tensor_name: str, tensor_type: int, shape: tuple[int, ...]
+) -> onnx.TensorProto:
+    """Create an external tensor.
+
+    Arguments:
+        location: unique identifier (not necessary a path)
+        tensor_name: tensor name in the graph
+        tensor_type: onnx type
+        shape: shape the of the initializer
+
+    Returns:
+        the created tensor
+    """
+    tensor_location = location
+    tensor = onnx.TensorProto()
+    tensor.name = tensor_name
+    _set_external_data(tensor, tensor_location)
+    tensor.data_type = tensor_type
+    tensor.dims.extend(shape)
+    return tensor
+
+
+class ModelContainer:
+    """Implements an API to store large tensors outside the main ModelProto,
+    it avoids copying large initializers when defining the model and these initializers
+    are never serialized through protobuf.
+    No tensor is stored on disk until the user explicitly saves the model.
+    """
+
+    def __init__(self) -> None:
+        self.model_proto_: onnx.ModelProto | None = None
+        self.large_initializers: dict[str, np.ndarray] = {}
+
+    def check_model(self):
+        if self.model_proto is not None:
+            onnx.checker.check_model(self.model_proto)
+
+    def __getitem__(self, name: str) -> np.ndarray:
+        """Returns an external tensor given its name."""
+        if name not in self.large_initializers:
+            raise ValueError(
+                f"Unable to find large tensor {name!r} among {sorted(self.large_initializers)}."
+            )
+        return self.large_initializers[name]
+
+    @property
+    def model_proto(self) -> onnx.ModelProto:
+        if self.model_proto_ is None:
+            raise RuntimeError("ModelContainer is empty.")
+        return self.model_proto_
+
+    @model_proto.setter
+    def model_proto(self, model_proto: onnx.ModelProto):
+        self.model_proto_ = model_proto
+        self.graphs_ = list(self.enumerate_graph_protos())
+
+    def enumerate_graph_protos(self) -> Iterable[onnx.GraphProto]:
+        """Enumerates all GraphProtos in a model."""
+        yield self.model_proto.graph
+        yield from _enumerate_subgraphs(self.model_proto.graph)
+
+    def is_in_memory_external_initializer(self, name: str) -> bool:
+        """Tells if an initializer name is an external initializer stored in memory.
+        The name must start with '#' in that case.
+        """
+        return name.startswith("#")
+
+    def set_large_initializers(self, large_initializers: dict[str, np.ndarray]):
+        """Adds all large tensors (not stored in the model)."""
+        for k in large_initializers:
+            if not self.is_in_memory_external_initializer(k):
+                raise ValueError(
+                    f"The location {k!r} must start with '#' to be ignored by check model."
+                )
+        self.large_initializers = large_initializers
+
+    def check_large_initializers(self) -> None:
+        for tensor in ext_data._get_all_tensors(self.model_proto):
+            if not ext_data.uses_external_data(tensor):
+                continue
+            prop: onnx.StringStringEntryProto | None = None
+            for ext in tensor.external_data:
+                if ext.key == "location":
+                    prop = ext
+            if prop is None:
+                raise RuntimeError(
+                    f"No location found for tensor name {tensor.name!r}."
+                )
+            if prop.value not in self.large_initializers:
+                raise RuntimeError(
+                    f"Unable to find large tensor named {tensor.name!r} "
+                    f"with location {prop.value!r} in "
+                    f"{sorted(self.large_initializers)}."
+                )
+
+    def _save_external(
+        self, file_path: str, all_tensors_to_one_file: bool
+    ) -> onnx.ModelProto:
+        """Save the large model into a main onnx file and one file
+        per tensor. Follows the same format as :func:`write_external_data_tensors
+        `.
+        The main model needs to be modified to update the file location,
+        the function returns this modified copy.
+
+        Arguments:
+            file_path: model file
+            all_tensors_to_one_file: all tensors in one file
+
+        Returns:
+            modified main model proto
+        """
+
+        def _clean_name(prefix: str, name: str, unique_names: dict[str, int]) -> str:
+            if prefix:
+                name = f"{prefix}-{name}"
+            for c in ":/\\;,!":
+                name = name.replace(c, "")
+            base_name = name
+            if name in unique_names:
+                i = unique_names[name] + 1
+                unique_names[name] = i
+                return f"{base_name}_{i}"
+            unique_names[name] = 1
+            return name
+
+        unique_names: dict[str, int] = {}
+        folder = os.path.dirname(file_path)
+        if not os.path.exists(folder):
+            raise FileNotFoundError(f"Folder {folder!r} does not exist.")
+        proto = self.model_proto.SerializeToString()
+        copy = onnx.ModelProto()
+        copy.ParseFromString(proto)
+        prefix = os.path.splitext(os.path.split(file_path)[-1])[0]
+
+        if all_tensors_to_one_file:
+            file_weight = f"{os.path.split(file_path)[1]}.weight"
+            full_file_weight = f"{file_path}.weight"
+            offset = 0
+            with open(full_file_weight, "wb") as f:
+                pass
+
+        for tensor in ext_data._get_all_tensors(copy):
+            if not ext_data.uses_external_data(tensor):
+                continue
+            prop: onnx.StringStringEntryProto | None = None
+            for ext in tensor.external_data:
+                if ext.key == "location":
+                    prop = ext
+            if prop is None:
+                raise RuntimeError(
+                    f"No location found for tensor name {tensor.name!r}."
+                )
+            if prop.value not in self.large_initializers:
+                raise RuntimeError(
+                    f"Unable to find large tensor named {tensor.name!r} "
+                    f"with location {prop.value!r} in "
+                    f"{sorted(self.large_initializers)}."
+                )
+            np_tensor = self.large_initializers[prop.value]
+
+            if sys.byteorder == "big":
+                # Convert endian from little to big
+                tensor_bytes = np_tensor.byteswap().tobytes()
+            else:
+                tensor_bytes = np_tensor.tobytes()
+            if all_tensors_to_one_file:
+                _set_external_data(
+                    tensor,
+                    location=file_weight,
+                    offset=offset,
+                    length=len(tensor_bytes),
+                )
+                offset += len(tensor_bytes)
+                with open(full_file_weight, "ab") as f:
+                    f.write(tensor_bytes)
+            else:
+                name = f"{_clean_name(prefix, prop.value, unique_names)}.weight"
+                _set_external_data(tensor, location=name)
+                full_name = os.path.join(folder, name)
+                prop.value = name
+                with open(full_name, "wb") as f:
+                    f.write(tensor_bytes)
+
+        with open(file_path, "wb") as f:
+            f.write(copy.SerializeToString())
+        return copy
+
+    def save(
+        self,
+        file_path: str,
+        all_tensors_to_one_file: bool = False,
+    ) -> onnx.ModelProto:
+        """Save the large model.
+        The function returns a ModelProto,
+        the current one if the model did not need any modification,
+        a modified copy of it if it required changes such as giving file names
+        to every external tensor.
+
+        Arguments:
+            file_path: model file
+            all_tensors_to_one_file: saves all large tensors in one file or
+                one file per lerge tensor
+
+        Returns:
+            the saved ModelProto
+        """
+        return self._save_external(
+            file_path, all_tensors_to_one_file=all_tensors_to_one_file
+        )
+
+    def load(self, file_path: str, load_large_initializers: bool = True):
+        """Load the large model.
+
+        Arguments:
+            file_path: model file
+            load_large_initializers: loads the large initializers,
+                if not done, the model is incomplete but it can be used to
+                look into the model without executing it and method
+                :meth:`_load_large_initializers` can be used to load them later
+        """
+        self.model_proto_ = onnx.load_model(file_path, load_external_data=False)
+        if load_large_initializers:
+            self._load_large_initializers(file_path)
+
+    def _load_large_initializers(self, file_path):
+        """Loads large initializers.
+
+        Arguments:
+            file_path: model file, the weight are expected to be in the same folder as this file
+        """
+        if self.model_proto_ is None:
+            raise RuntimeError("A model must be loaded before loading the weights.")
+        self.large_initializers = {}
+        base_dir = os.path.dirname(file_path)
+        for i, tensor in enumerate(ext_data._get_all_tensors(self.model_proto_)):
+            if not ext_data.uses_external_data(tensor):
+                continue
+
+            info = ext_data.ExternalDataInfo(tensor)
+            external_data_file_path = c_checker._resolve_external_data_location(  # type: ignore[attr-defined]
+                base_dir, info.location, tensor.name
+            )
+            key = f"#t{i}"
+            _set_external_data(tensor, location=key)
+
+            with open(external_data_file_path, "rb") as data_file:
+                if info.offset:
+                    data_file.seek(info.offset)
+
+                raw_data = (
+                    data_file.read(info.length) if info.length else data_file.read()
+                )
+
+                dtype = onnx.helper.tensor_dtype_to_np_dtype(tensor.data_type)
+                shape = tuple(tensor.dims)
+
+                if sys.byteorder == "big":
+                    np_tensor = (
+                        np.frombuffer(raw_data, dtype=dtype).byteswap().reshape(shape)
+                    )
+                else:
+                    np_tensor = np.frombuffer(raw_data, dtype=dtype).reshape(shape)
+
+                self.large_initializers[key] = np_tensor
+
+
+def make_large_model(
+    graph: onnx.GraphProto,
+    large_initializers: dict[str, np.ndarray] | None = None,
+    **kwargs: Any,
+) -> ModelContainer:
+    """Construct a ModelContainer
+
+    C API and Python API of protobuf do not operate without serializing
+    the protos. This function uses the Python API of ModelContainer.
+
+    Arguments:
+        graph: *make_graph* returns
+        large_initializers: dictionary `name: large tensor`,
+            large tensor is any python object supporting the DLPack protocol,
+            the ownership the tensor is transferred to the ModelContainer,
+            the tensor must define method `tobytes` like numpy tensors
+        **kwargs: any attribute to add to the returned instance
+
+    Returns:
+        ModelContainer
+    """
+    model = onnx.helper.make_model(graph, **kwargs)
+    large_model = ModelContainer()
+    large_model.model_proto = model
+    if large_initializers:
+        large_model.set_large_initializers(large_initializers)
+        large_model.check_large_initializers()
+    return large_model
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/numpy_helper.py b/pythonProject/.venv/Lib/site-packages/onnx/numpy_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c7e49d9434c9eeffcd29016bc993e7136257ced
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/numpy_helper.py
@@ -0,0 +1,812 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING, Any
+
+import ml_dtypes
+import numpy as np
+import numpy.typing as npt
+import typing_extensions
+
+import onnx.external_data_helper
+from onnx import helper, subbyte
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+# System is little endian
+_IS_LITTLE_ENDIAN = sys.byteorder == "little"
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def bfloat16_to_float32(
+    data: np.int16 | np.int32 | np.ndarray,
+    dims: int | Sequence[int] | None = None,
+) -> np.ndarray:
+    """Converts ndarray of bf16 (as uint32) to f32 (as uint32).
+
+    Args:
+        data: A numpy array, empty dimensions are allowed if dims is
+            None.
+        dims: If specified, the function reshapes the results.
+
+    Returns:
+        A numpy array of float32 with the same dimension if dims is
+        None, or reshaped to dims if specified
+    """
+    shift = lambda x: x << 16  # noqa: E731
+    if dims is None:
+        if len(data.shape) == 0:
+            return shift(np.array([data]).astype(np.int32)).view(np.float32)[0]  # type: ignore[no-any-return]
+        return shift(data.astype(np.int32)).view(np.float32)  # type: ignore[no-any-return]
+    return shift(data.astype(np.int32)).reshape(dims).view(np.float32)  # type: ignore[no-any-return]
+
+
+def _float8e4m3_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
+    if not fn:
+        raise NotImplementedError("fn=False is not implemented.")
+    if ival < 0 or ival > 255:  # noqa: PLR2004
+        raise ValueError(f"{ival} is not a float8.")
+    if uz:
+        exponent_bias = 8
+        if ival == 0x80:  # noqa: PLR2004
+            return np.nan  # type: ignore[return-value]
+    else:
+        exponent_bias = 7
+        if ival == 255:  # noqa: PLR2004
+            return np.float32(-np.nan)
+        if ival == 127:  # noqa: PLR2004
+            return np.float32(np.nan)
+
+    ival = np.uint32(ival)  # type: ignore[assignment]
+    expo = (ival & 0x78) >> 3
+    mant = ival & 0x07
+    sign = ival & 0x80
+    res = sign << 24
+    if expo == 0:
+        if mant > 0:
+            expo = 0x7F - exponent_bias
+            if mant & 0x4 == 0:
+                mant &= 0x3
+                mant <<= 1
+                expo -= 1
+            if mant & 0x4 == 0:
+                mant &= 0x3
+                mant <<= 1
+                expo -= 1
+            res |= (mant & 0x3) << 21
+            res |= expo << 23
+    else:
+        res |= mant << 20
+        expo += 0x7F - exponent_bias
+        res |= expo << 23
+    f = np.uint32(res).view(np.float32)
+    return f
+
+
+_float8e4m3_to_float32 = np.vectorize(
+    _float8e4m3_to_float32_scalar, excluded=["fn", "uz"]
+)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float8e4m3_to_float32(
+    data: np.int16 | np.int32 | np.ndarray,
+    dims: int | Sequence[int] | None = None,
+    fn: bool = True,
+    uz: bool = False,
+) -> np.ndarray:
+    """Converts ndarray of float8, e4m3 (as uint32) to f32 (as uint32).
+
+    See :ref:`onnx-detail-float8` for technical details.
+
+    Args:
+        data: A numpy array, empty dimensions are allowed if dims is None.
+        dims: If specified, the function reshapes the results.
+        fn: No infinite values.
+        uz: No negative zero.
+
+    Returns:
+        A numpy array of float32 with the same dimension if dims is None,
+        or reshaped to dims if specified.
+    """
+    if not fn:
+        raise NotImplementedError(
+            "float32_to_float8e4m3 not implemented with fn=False."
+        )
+    res = _float8e4m3_to_float32(data, fn=fn, uz=uz)
+    if dims is None:
+        return res  # type: ignore[no-any-return]
+    return res.reshape(dims)  # type: ignore[no-any-return]
+
+
+def _float8e5m2_to_float32_scalar(ival: int, fn: bool, uz: bool) -> np.float32:
+    if fn and uz:
+        if ival == 0x80:  # noqa: PLR2004
+            return np.float32(np.nan)
+        exponent_bias = 16
+    elif not fn and not uz:
+        if ival in {253, 254, 255}:
+            return np.float32(-np.nan)
+        if ival in {125, 126, 127}:
+            return np.float32(np.nan)
+        if ival == 252:  # noqa: PLR2004
+            return np.float32(-np.inf)
+        if ival == 124:  # noqa: PLR2004
+            return np.float32(np.inf)
+        exponent_bias = 15
+    else:
+        raise NotImplementedError("fn and uz must be both False or True.")
+
+    ival = np.uint32(ival)  # type: ignore[assignment]
+    expo = (ival & 0x7C) >> 2
+    mant = ival & 0x03
+    sign = ival & 0x80
+    res = sign << 24
+    if expo == 0:
+        if mant > 0:
+            expo = 0x7F - exponent_bias
+            if mant & 0x2 == 0:
+                mant &= 0x1
+                mant <<= 1
+                expo -= 1
+            res |= (mant & 0x1) << 22
+            res |= expo << 23
+    else:
+        res |= mant << 21
+        expo += 0x7F - exponent_bias
+        res |= expo << 23
+    f = np.uint32(res).view(np.float32)
+    return f
+
+
+_float8e5m2_to_float32 = np.vectorize(
+    _float8e5m2_to_float32_scalar, excluded=["fn", "uz"]
+)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float8e5m2_to_float32(
+    data: np.int16 | np.int32 | np.ndarray,
+    dims: int | Sequence[int] | None = None,
+    fn: bool = False,
+    uz: bool = False,
+) -> np.ndarray:
+    """Converts ndarray of float8, e5m2 (as uint32) to f32 (as uint32).
+
+    See :ref:`onnx-detail-float8` for technical details.
+
+    Args:
+        data: A numpy array, empty dimensions are allowed if dims is None.
+        dims: If specified, the function reshapes the results.
+        fn: No infinite values.
+        uz: No negative zero.
+
+    Returns:
+        A numpy array of float32 with the same dimension if dims is None,
+        or reshaped to dims if specified
+    """
+    res = _float8e5m2_to_float32(data, fn=fn, uz=uz)
+    if dims is None:
+        return res  # type: ignore[no-any-return]
+    return res.reshape(dims)  # type: ignore[no-any-return]
+
+
+def to_float8e8m0(
+    x: np.ndarray,
+    saturate: bool = True,
+    round_mode: str = "up",
+) -> np.ndarray:
+    """Convert float32 NumPy array to float8e8m0 representation. If the input
+    is not a float32 array, it will be cast to one first.
+
+    Args:
+        x: Input array to convert.
+        saturate: Whether to saturate at max/min float8e8m0 value.
+        round_mode: "nearest", "up", or "down".
+
+    Returns:
+        np.ndarray: Array of ml_dtypes.float8_e8m0fnu values.
+    """
+    x_f32 = np.asarray(x, dtype=np.float32)
+    f_bits = x_f32.view(np.uint32)
+
+    # Extract exponent bits
+    exponent = (f_bits >> 23) & 0xFF
+    exponent = exponent.astype(
+        np.uint16
+    )  # use uint16 to prevent overflow during computation
+
+    # Identify NaN or Inf
+    special_mask = exponent == 0xFF  # noqa: PLR2004
+    output = np.zeros_like(exponent, dtype=np.uint8)
+    output[special_mask] = 0xFF  # Preserve NaN/Inf as max exponent
+
+    # Process normal numbers
+    normal_mask = ~special_mask
+
+    if round_mode == "nearest":
+        # Get guard, round, sticky, and least significant bits
+        g = ((f_bits & 0x400000) > 0).astype(np.uint8)
+        r = ((f_bits & 0x200000) > 0).astype(np.uint8)
+        s = ((f_bits & 0x1FFFFF) > 0).astype(np.uint8)
+        lsb = (exponent > 0).astype(np.uint8)
+
+        round_up = (g == 1) & ((r == 1) | (s == 1) | (lsb == 1))
+
+        increment = np.zeros_like(exponent)
+        increment[round_up & normal_mask] = 1
+
+        if saturate:
+            max_mask = (exponent == 0xFE) & round_up & normal_mask  # noqa: PLR2004
+            increment[max_mask] = 0  # Don't overflow past max value
+
+        exponent += increment
+
+    elif round_mode == "up":
+        has_fraction = (f_bits & 0x4FFFFF) > 0
+        round_up = has_fraction & normal_mask
+
+        if saturate:
+            max_mask = (exponent == 0xFE) & round_up  # noqa: PLR2004
+            round_up[max_mask] = False
+
+        exponent += round_up.astype(np.uint16)
+
+    elif round_mode == "down":
+        pass  # No rounding needed
+
+    else:
+        raise ValueError(f"Unsupported rounding mode: {round_mode}")
+
+    # Clip exponent to uint8 range
+    exponent = exponent.astype(np.uint8)
+
+    output[normal_mask] = exponent[normal_mask]
+
+    return output.view(ml_dtypes.float8_e8m0fnu)
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider implementing your own unpack logic",
+    category=DeprecationWarning,
+)
+def unpack_int4(
+    data: np.int32 | np.ndarray,
+    dims: int | Sequence[int],
+    signed: bool,
+) -> np.ndarray:
+    """Converts ndarray of int4 (as packed uint8) to f32
+    See :ref:`onnx-detail-int4` for technical details.
+
+    Args:
+        data: A numpy array, empty dimensions are allowed if dims is
+            None.
+        dims: The dimensions are used to reshape the unpacked buffer
+        signed: Whether the 4 bit integer is signed or unsigned
+
+    Returns:
+        A numpy array of float32 reshaped to dims.
+    """
+    single_func = lambda x: subbyte.unpack_single_4bitx2(x, signed)  # noqa: E731
+    func = np.frompyfunc(single_func, 1, 2)
+
+    res_high, res_low = func(data.ravel())
+    res = np.empty((res_high.size + res_low.size,), dtype=np.float32)
+    res[0::2] = res_high
+    res[1::2] = res_low
+
+    if (
+        res.size == np.prod(dims) + 1
+    ):  # handle single-element padding due to odd number of elements
+        res = res.ravel()[:-1]
+    res = res.reshape(dims)
+    return res
+
+
+def _unpacked_float4e2m1_to_float32(
+    x: npt.NDArray[np.uint8],
+) -> npt.NDArray[np.float32]:
+    """Evaluate the numerical value of an array of unpacked float4e2m1 values (as uint8)
+    See :ref:`onnx-detail-int4` for technical details.
+
+    Args:
+        x: an array of uint8 elements representing a float4e2m1 (using the 4 LSB)
+
+    Returns:
+        An array of float32 elements representing the values of the float4e2m1 input.
+    """
+    # x is stored in 4 LSB of int
+    sign = np.where(np.bitwise_and(x, 0x08), -1, 1)
+    mantissa = (x & 0x01).astype(np.float32)
+    exponent = ((x & 0x06) >> 1).astype(np.float32)
+
+    val = np.where(
+        exponent == 0,
+        sign * (mantissa / 2.0),
+        sign * (1.0 + mantissa / 2.0) * 2.0 ** (exponent - 1),
+    )  # denormalized, normalized
+    return val
+
+
+def _unpack_4bit(
+    data: npt.NDArray[np.uint8], dims: Sequence[int]
+) -> npt.NDArray[np.uint8]:
+    """Convert a packed uint4 array to unpacked uint4 array represented as uint8.
+
+    Args:
+        data: A numpy array.
+        dims: The dimensions are used to reshape the unpacked buffer.
+
+    Returns:
+        A numpy array of int8/uint8 reshaped to dims.
+    """
+    result = np.empty([data.size * 2], dtype=data.dtype)
+    array_low = data & np.uint8(0x0F)
+    array_high = data & np.uint8(0xF0)
+    array_high >>= np.uint8(4)
+    result[0::2] = array_low
+    result[1::2] = array_high
+    if result.size == np.prod(dims) + 1:
+        # handle single-element padding due to odd number of elements
+        result = result[:-1]
+    result.resize(dims, refcheck=False)
+    return result
+
+
+def _pack_4bitx2(array: np.ndarray) -> npt.NDArray[np.uint8]:
+    """Convert a numpy array to flatten, packed int4/uint4. Elements must be in the correct range."""
+    # Create a 1D copy
+    array_flat = array.ravel().view(np.uint8).copy()
+    size = array.size
+    odd_sized = size % 2 == 1
+    if odd_sized:
+        array_flat.resize([size + 1], refcheck=False)
+    array_flat &= 0x0F
+    array_flat[1::2] <<= 4
+    return array_flat[0::2] | array_flat[1::2]  # type: ignore[return-type]
+
+
+def to_array(tensor: onnx.TensorProto, base_dir: str = "") -> np.ndarray:  # noqa: PLR0911
+    """Converts a tensor def object to a numpy array.
+
+    This function uses ml_dtypes if the dtype is not a native numpy dtype.
+
+    Args:
+        tensor: a TensorProto object.
+        base_dir: if external tensor exists, base_dir can help to find the path to it
+
+    Returns:
+        arr: the converted array.
+    """
+    if tensor.HasField("segment"):
+        raise ValueError("Currently not supporting loading segments.")
+    if tensor.data_type == onnx.TensorProto.UNDEFINED:
+        raise TypeError("The element type in the input tensor is UNDEFINED.")
+
+    tensor_dtype = tensor.data_type
+    np_dtype = helper.tensor_dtype_to_np_dtype(tensor_dtype)
+    storage_np_dtype = helper.tensor_dtype_to_np_dtype(
+        helper.tensor_dtype_to_storage_tensor_dtype(tensor_dtype)
+    )
+    storage_field = helper.tensor_dtype_to_field(tensor_dtype)
+    dims = tensor.dims
+
+    if tensor.data_type == onnx.TensorProto.STRING:
+        utf8_strings = getattr(tensor, storage_field)
+        ss = [s.decode("utf-8") for s in utf8_strings]
+        return np.asarray(ss).astype(np_dtype).reshape(dims)
+
+    # Load raw data from external tensor if it exists
+    if onnx.external_data_helper.uses_external_data(tensor):
+        onnx.external_data_helper.load_external_data_for_tensor(tensor, base_dir)
+
+    if tensor.HasField("raw_data"):
+        # Raw_bytes support: using frombuffer.
+        raw_data = tensor.raw_data
+        if sys.byteorder == "big":
+            # Convert endian from little to big
+            raw_data = np.frombuffer(raw_data, dtype=np_dtype).byteswap().tobytes()
+
+        if tensor_dtype in {
+            onnx.TensorProto.INT4,
+            onnx.TensorProto.UINT4,
+            onnx.TensorProto.FLOAT4E2M1,
+        }:
+            data = np.frombuffer(raw_data, dtype=np.uint8)
+            return _unpack_4bit(data, dims).view(np_dtype)
+
+        return np.frombuffer(raw_data, dtype=np_dtype).reshape(dims)
+
+    if tensor_dtype in {
+        onnx.TensorProto.BFLOAT16,
+        onnx.TensorProto.FLOAT16,
+        onnx.TensorProto.INT16,
+        onnx.TensorProto.UINT16,
+    }:
+        return (
+            np.array(tensor.int32_data, dtype=np.int32)
+            .view(np.uint32)
+            .astype(np.uint16)
+            .reshape(dims)
+            .view(np_dtype)
+        )
+
+    if tensor_dtype in {
+        onnx.TensorProto.FLOAT8E4M3FN,
+        onnx.TensorProto.FLOAT8E4M3FNUZ,
+        onnx.TensorProto.FLOAT8E5M2,
+        onnx.TensorProto.FLOAT8E5M2FNUZ,
+        onnx.TensorProto.FLOAT8E8M0,
+        onnx.TensorProto.BOOL,
+    }:
+        return (
+            np.array(tensor.int32_data, dtype=np.int32)
+            .view(np.uint32)
+            .astype(np.uint8)
+            .view(np_dtype)
+            .reshape(dims)
+        )
+
+    if tensor_dtype in {
+        onnx.TensorProto.UINT4,
+        onnx.TensorProto.INT4,
+        onnx.TensorProto.FLOAT4E2M1,
+    }:
+        data = (
+            np.array(tensor.int32_data, dtype=np.int32).view(np.uint32).astype(np.uint8)
+        )
+        return _unpack_4bit(data, dims).view(np_dtype)
+
+    data = getattr(tensor, storage_field)
+    if tensor_dtype in (onnx.TensorProto.COMPLEX64, onnx.TensorProto.COMPLEX128):
+        return np.array(data, dtype=storage_np_dtype).view(dtype=np_dtype).reshape(dims)
+
+    return np.asarray(data, dtype=storage_np_dtype).astype(np_dtype).reshape(dims)
+
+
+def from_array(array: np.ndarray, /, name: str | None = None) -> onnx.TensorProto:
+    """Converts an array into a TensorProto including
+
+    Args:
+        array: a numpy array.
+        name: (optional) the name of the tensor.
+
+    Returns:
+        TensorProto: the converted tensor def.
+    """
+    tensor = onnx.TensorProto()
+    tensor.dims.extend(array.shape)
+    if name:
+        tensor.name = name
+    if array.dtype == object or np.issubdtype(array.dtype, np.str_):
+        # Special care for strings.
+        tensor.data_type = onnx.TensorProto.STRING
+        # TODO: Introduce full string support.
+        # We flatten the array in case there are n-D arrays are specified
+        # If you want more complex shapes then follow the below instructions.
+        # Unlike other types where the shape is automatically inferred from
+        # nested arrays of values, the only reliable way now to feed strings
+        # is to put them into a flat array then specify type astype(object)
+        # (otherwise all strings may have different types depending on their length)
+        # and then specify shape .reshape([x, y, z])
+        flat_array = array.flatten()
+        for e in flat_array:
+            if isinstance(e, str):
+                tensor.string_data.append(e.encode("utf-8"))
+            elif isinstance(e, bytes):
+                tensor.string_data.append(e)
+            else:
+                raise NotImplementedError(
+                    "Unrecognized object in the object array, expect a string, or array of bytes: ",
+                    str(type(e)),
+                )
+        return tensor
+
+    dtype = helper.np_dtype_to_tensor_dtype(array.dtype)
+    if dtype in {
+        onnx.TensorProto.INT4,
+        onnx.TensorProto.UINT4,
+        onnx.TensorProto.FLOAT4E2M1,
+    }:
+        # Pack the array into int4
+        array = _pack_4bitx2(array)
+    if not _IS_LITTLE_ENDIAN:
+        array = array.view(array.dtype.newbyteorder("<"))
+
+    tensor.raw_data = array.tobytes()
+    tensor.data_type = dtype
+    return tensor
+
+
+def to_list(sequence: onnx.SequenceProto) -> list[Any]:
+    """Converts a sequence def to a Python list.
+
+    Args:
+        sequence: a SequenceProto object.
+
+    Returns:
+        list: the converted list.
+    """
+    elem_type = sequence.elem_type
+    if elem_type == onnx.SequenceProto.TENSOR:
+        return [to_array(v) for v in sequence.tensor_values]
+    if elem_type == onnx.SequenceProto.SPARSE_TENSOR:
+        return [to_array(v) for v in sequence.sparse_tensor_values]  # type: ignore[arg-type]
+    if elem_type == onnx.SequenceProto.SEQUENCE:
+        return [to_list(v) for v in sequence.sequence_values]
+    if elem_type == onnx.SequenceProto.MAP:
+        return [to_dict(v) for v in sequence.map_values]
+    raise TypeError("The element type in the input sequence is not supported.")
+
+
+def from_list(
+    lst: list[Any], name: str | None = None, dtype: int | None = None
+) -> onnx.SequenceProto:
+    """Converts a list into a sequence def.
+
+    Args:
+        lst: a Python list
+        name: (optional) the name of the sequence.
+        dtype: (optional) type of element in the input list, used for specifying
+                          sequence values when converting an empty list.
+
+    Returns:
+        SequenceProto: the converted sequence def.
+    """
+    sequence = onnx.SequenceProto()
+    if name:
+        sequence.name = name
+
+    if dtype:
+        elem_type = dtype
+    elif len(lst) > 0:
+        first_elem = lst[0]
+        if isinstance(first_elem, dict):
+            elem_type = onnx.SequenceProto.MAP
+        elif isinstance(first_elem, list):
+            elem_type = onnx.SequenceProto.SEQUENCE
+        else:
+            elem_type = onnx.SequenceProto.TENSOR
+    else:
+        # if empty input list and no dtype specified
+        # choose sequence of tensors on default
+        elem_type = onnx.SequenceProto.TENSOR
+    sequence.elem_type = elem_type
+
+    if (len(lst) > 0) and not all(isinstance(elem, type(lst[0])) for elem in lst):
+        raise TypeError(
+            "The element type in the input list is not the same "
+            "for all elements and therefore is not supported as a sequence."
+        )
+
+    if elem_type == onnx.SequenceProto.TENSOR:
+        for tensor in lst:
+            sequence.tensor_values.extend([from_array(np.asarray(tensor))])
+    elif elem_type == onnx.SequenceProto.SEQUENCE:
+        for seq in lst:
+            sequence.sequence_values.extend([from_list(seq)])
+    elif elem_type == onnx.SequenceProto.MAP:
+        for mapping in lst:
+            sequence.map_values.extend([from_dict(mapping)])
+    else:
+        raise TypeError(
+            "The element type in the input list is not a tensor, "
+            "sequence, or map and is not supported."
+        )
+    return sequence
+
+
+def to_dict(map_proto: onnx.MapProto) -> dict[Any, Any]:
+    """Converts a map def to a Python dictionary.
+
+    Args:
+        map_proto: a MapProto object.
+
+    Returns:
+        The converted dictionary.
+    """
+    key_list: list[Any] = []
+    if map_proto.key_type == onnx.TensorProto.STRING:
+        key_list = list(map_proto.string_keys)
+    else:
+        key_list = list(map_proto.keys)
+
+    value_list = to_list(map_proto.values)
+    if len(key_list) != len(value_list):
+        raise IndexError(
+            "Length of keys and values for MapProto (map name: ",
+            map_proto.name,
+            ") are not the same.",
+        )
+    dictionary = dict(zip(key_list, value_list))
+    return dictionary
+
+
+def from_dict(dict_: dict[Any, Any], name: str | None = None) -> onnx.MapProto:
+    """Converts a Python dictionary into a map def.
+
+    Args:
+        dict_: Python dictionary
+        name: (optional) the name of the map.
+
+    Returns:
+        MapProto: the converted map def.
+    """
+    map_proto = onnx.MapProto()
+    if name:
+        map_proto.name = name
+    keys = list(dict_)
+    raw_key_type = np.result_type(keys[0])
+    key_type = helper.np_dtype_to_tensor_dtype(raw_key_type)
+
+    valid_key_int_types = {
+        onnx.TensorProto.INT8,
+        onnx.TensorProto.INT16,
+        onnx.TensorProto.INT32,
+        onnx.TensorProto.INT64,
+        onnx.TensorProto.UINT8,
+        onnx.TensorProto.UINT16,
+        onnx.TensorProto.UINT32,
+        onnx.TensorProto.UINT64,
+    }
+
+    if not (all(np.result_type(key) == raw_key_type for key in keys)):
+        raise TypeError(
+            "The key type in the input dictionary is not the same "
+            "for all keys and therefore is not valid as a map."
+        )
+
+    values = list(dict_.values())
+    raw_value_type = np.result_type(values[0])
+    if not all(np.result_type(val) == raw_value_type for val in values):
+        raise TypeError(
+            "The value type in the input dictionary is not the same "
+            "for all values and therefore is not valid as a map."
+        )
+
+    value_seq = from_list(values)
+
+    map_proto.key_type = key_type
+    if key_type == onnx.TensorProto.STRING:
+        map_proto.string_keys.extend(keys)
+    elif key_type in valid_key_int_types:
+        map_proto.keys.extend(keys)
+    map_proto.values.CopyFrom(value_seq)
+    return map_proto
+
+
+def to_optional(optional: onnx.OptionalProto) -> Any | None:
+    """Converts an optional def to a Python optional.
+
+    Args:
+        optional: an OptionalProto object.
+
+    Returns:
+        opt: the converted optional.
+    """
+    elem_type = optional.elem_type
+    if elem_type == onnx.OptionalProto.UNDEFINED:
+        return None
+    if elem_type == onnx.OptionalProto.TENSOR:
+        return to_array(optional.tensor_value)
+    if elem_type == onnx.OptionalProto.SPARSE_TENSOR:
+        return to_array(optional.sparse_tensor_value)  # type: ignore[arg-type]
+    if elem_type == onnx.OptionalProto.SEQUENCE:
+        return to_list(optional.sequence_value)
+    if elem_type == onnx.OptionalProto.MAP:
+        return to_dict(optional.map_value)
+    if elem_type == onnx.OptionalProto.OPTIONAL:
+        return to_optional(optional.optional_value)
+    raise TypeError("The element type in the input optional is not supported.")
+
+
+def from_optional(
+    opt: Any | None, name: str | None = None, dtype: int | None = None
+) -> onnx.OptionalProto:
+    """Converts an optional value into a Optional def.
+
+    Args:
+        opt: a Python optional
+        name: (optional) the name of the optional.
+        dtype: (optional) type of element in the input, used for specifying
+                          optional values when converting empty none. dtype must
+                          be a valid OptionalProto.DataType value
+
+    Returns:
+        optional: the converted optional def.
+    """
+    # TODO: create a map and replace conditional branches
+    optional = onnx.OptionalProto()
+    if name:
+        optional.name = name
+
+    if dtype is not None:
+        # dtype must be a valid onnx.OptionalProto.DataType
+        if dtype not in onnx.OptionalProto.DataType.values():
+            raise TypeError(f"{dtype} must be a valid OptionalProto.DataType.")
+        elem_type = dtype
+    elif isinstance(opt, dict):
+        elem_type = onnx.OptionalProto.MAP
+    elif isinstance(opt, list):
+        elem_type = onnx.OptionalProto.SEQUENCE
+    elif opt is None:
+        elem_type = onnx.OptionalProto.UNDEFINED
+    else:
+        elem_type = onnx.OptionalProto.TENSOR
+
+    optional.elem_type = elem_type
+
+    if opt is not None:
+        if elem_type == onnx.OptionalProto.TENSOR:
+            optional.tensor_value.CopyFrom(from_array(opt))
+        elif elem_type == onnx.OptionalProto.SEQUENCE:
+            optional.sequence_value.CopyFrom(from_list(opt))
+        elif elem_type == onnx.OptionalProto.MAP:
+            optional.map_value.CopyFrom(from_dict(opt))
+        else:
+            raise TypeError(
+                "The element type in the input is not a tensor, "
+                "sequence, or map and is not supported."
+            )
+    return optional
+
+
+def create_random_int(
+    input_shape: tuple[int], dtype: np.dtype, seed: int = 1
+) -> np.ndarray:
+    """Create random integer array for backend/test/case/node.
+
+    Args:
+        input_shape: The shape for the returned integer array.
+        dtype: The NumPy data type for the returned integer array.
+        seed: The seed for np.random.
+
+    Returns:
+        np.ndarray: Random integer array.
+    """
+    np.random.seed(seed)
+    if dtype in (
+        np.uint8,
+        np.uint16,
+        np.uint32,
+        np.uint64,
+        np.int8,
+        np.int16,
+        np.int32,
+        np.int64,
+    ):
+        # the range of np.random.randint is int32; set a fixed boundary if overflow
+        end = min(np.iinfo(dtype).max, np.iinfo(np.int32).max)
+        start = max(np.iinfo(dtype).min, np.iinfo(np.int32).min)
+        return np.random.randint(start, end, size=input_shape).astype(dtype)
+    else:
+        raise TypeError(f"{dtype} is not supported by create_random_int.")
+
+
+def saturate_cast(x: np.ndarray, dtype: np.dtype) -> np.ndarray:
+    """Saturate cast for numeric types.
+
+    This function ensures that values outside the representable range
+    of the target dtype are clamped to the maximum or minimum representable
+    value of that dtype.
+    """
+    if np.issubdtype(dtype, np.integer) or dtype in (ml_dtypes.int4, ml_dtypes.uint4):
+        info = ml_dtypes.iinfo(dtype)
+        x = np.round(x)
+    else:
+        info = ml_dtypes.finfo(dtype)  # type: ignore[assignment]
+
+    return np.clip(x, info.min, info.max).astype(dtype)
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.in.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.in.proto
new file mode 100644
index 0000000000000000000000000000000000000000..ec0f27b23c86851de3f5fdb6bb35501bd5c9ca52
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.in.proto
@@ -0,0 +1,150 @@
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package {PACKAGE_NAME};
+// #if ONNX-ML
+import "onnx/onnx-ml.proto";
+// #else
+import "onnx/onnx.proto";
+// #endif
+
+// This file contains the proto definitions for MapProto and
+// SequenceProto. These protos are used to represent the data structures
+// of maps and sequence for use in test data or ModelProto.
+
+// Sequences
+//
+// Defines a dense, ordered, collection of elements that are of homogeneous types.
+// Sequences can be made out of tensors, maps, or sequences.
+//
+// If a sequence is made out of tensors, the tensors must have the same element
+// type (i.e. int32). In some cases, the tensors in a sequence can have different
+// shapes.  Whether the tensors can have different shapes or not depends on the
+// type/shape associated with the corresponding "ValueInfo". For example,
+// "Sequence" means that all tensors have same shape. However,
+// "Sequence" means they can have different
+// shapes (all of rank 2), where "omitted" means the corresponding dimension has
+// no symbolic/constant value. Finally, "Sequence>" means
+// that the different tensors can have different ranks, when the "shape" itself
+// is omitted from the tensor-type. For a more complete description, refer to
+// https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes.
+//
+message SequenceProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element.
+  // This field MUST have a valid SequenceProto.DataType value
+  optional int32 elem_type = 2;
+
+  // For TensorProto values.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  repeated TensorProto tensor_values = 3;
+
+  // For SparseTensorProto values.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  repeated SparseTensorProto sparse_tensor_values = 4;
+
+  // For SequenceProto values, allowing sequences to be of themselves.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  repeated SequenceProto sequence_values = 5;
+
+  // For MapProto values.
+  // When this field is present, the elem_type field MUST be MAP.
+  repeated MapProto map_values = 6;
+
+  // For OptionalProto values.
+  // When this field is present, the elem_type field MUST be Optional.
+  repeated OptionalProto optional_values = 7;
+
+}
+
+
+// Maps
+//
+// Specifies an associative table, defined by keys and values.
+// MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32,
+// INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR,
+// SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain
+// the same throughout the instantiation of the MapProto.
+//
+message MapProto {
+
+  optional string name = 1;
+
+  // All MapProto data types must have the same length of keys and values.
+
+  // The data type of the key.
+  // This field MUST have a valid TensorProto.DataType value of
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING
+  optional int32 key_type = 2;
+
+  // Every element of keys has to be one of the following data types
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING.
+  // The integer cases are represented by the repeated int64 field keys below.
+  repeated int64 keys = 3;
+
+  // If keys are strings, they are represented by the repeated bytes field
+  // string_keys below.
+  repeated bytes string_keys = 4;
+
+  // MapProto values are represented in a SequenceProto of the same length as the
+  // repeated keys field and have to be one of the following data types
+  // TENSOR, SPARSE_TENSOR, MAP, SEQUENCE.
+  optional SequenceProto values = 5;
+}
+
+// Optional
+//
+//
+message OptionalProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element, identifies if the OptionalProto value
+  // is Tensor, Sparse Tensor, Sequence, Map, or Optional.
+  // The type of the optional value MUST match the elem_type specified.
+  // This field MUST have a valid OptionalProto.DataType value.
+  optional int32 elem_type = 2;
+
+  // For TensorProto value.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  optional TensorProto tensor_value = 3;
+
+  // For SparseTensorProto value.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  optional SparseTensorProto sparse_tensor_value = 4;
+
+  // For SequenceProto value.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  optional SequenceProto sequence_value = 5;
+
+  // For MapProto value.
+  // When this field is present, the elem_type field MUST be MAP.
+  optional MapProto map_value = 6;
+
+  // For OptionalProto value, allowing optional to be of itself (completeness)
+  // When this field is present, the elem_type field MUST be OPTIONAL.
+  optional OptionalProto optional_value = 7;
+
+}
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.proto
new file mode 100644
index 0000000000000000000000000000000000000000..61bba91732db50b403ff5609c152782a51573984
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-data.proto
@@ -0,0 +1,155 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx-ml.proto";
+
+// This file contains the proto definitions for MapProto and
+// SequenceProto. These protos are used to represent the data structures
+// of maps and sequence for use in test data or ModelProto.
+
+// Sequences
+//
+// Defines a dense, ordered, collection of elements that are of homogeneous types.
+// Sequences can be made out of tensors, maps, or sequences.
+//
+// If a sequence is made out of tensors, the tensors must have the same element
+// type (i.e. int32). In some cases, the tensors in a sequence can have different
+// shapes.  Whether the tensors can have different shapes or not depends on the
+// type/shape associated with the corresponding "ValueInfo". For example,
+// "Sequence" means that all tensors have same shape. However,
+// "Sequence" means they can have different
+// shapes (all of rank 2), where "omitted" means the corresponding dimension has
+// no symbolic/constant value. Finally, "Sequence>" means
+// that the different tensors can have different ranks, when the "shape" itself
+// is omitted from the tensor-type. For a more complete description, refer to
+// https://github.com/onnx/onnx/blob/main/docs/IR.md#static-tensor-shapes.
+//
+message SequenceProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element.
+  // This field MUST have a valid SequenceProto.DataType value
+  optional int32 elem_type = 2;
+
+  // For TensorProto values.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  repeated TensorProto tensor_values = 3;
+
+  // For SparseTensorProto values.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  repeated SparseTensorProto sparse_tensor_values = 4;
+
+  // For SequenceProto values, allowing sequences to be of themselves.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  repeated SequenceProto sequence_values = 5;
+
+  // For MapProto values.
+  // When this field is present, the elem_type field MUST be MAP.
+  repeated MapProto map_values = 6;
+
+  // For OptionalProto values.
+  // When this field is present, the elem_type field MUST be Optional.
+  repeated OptionalProto optional_values = 7;
+
+}
+
+
+// Maps
+//
+// Specifies an associative table, defined by keys and values.
+// MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32,
+// INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR,
+// SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain
+// the same throughout the instantiation of the MapProto.
+//
+message MapProto {
+
+  optional string name = 1;
+
+  // All MapProto data types must have the same length of keys and values.
+
+  // The data type of the key.
+  // This field MUST have a valid TensorProto.DataType value of
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING
+  optional int32 key_type = 2;
+
+  // Every element of keys has to be one of the following data types
+  // INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING.
+  // The integer cases are represented by the repeated int64 field keys below.
+  repeated int64 keys = 3;
+
+  // If keys are strings, they are represented by the repeated bytes field
+  // string_keys below.
+  repeated bytes string_keys = 4;
+
+  // MapProto values are represented in a SequenceProto of the same length as the
+  // repeated keys field and have to be one of the following data types
+  // TENSOR, SPARSE_TENSOR, MAP, SEQUENCE.
+  optional SequenceProto values = 5;
+}
+
+// Optional
+//
+//
+message OptionalProto {
+
+  optional string name = 1;
+
+  enum DataType {
+    UNDEFINED = 0;
+    TENSOR = 1;
+    SPARSE_TENSOR = 2;
+    SEQUENCE = 3;
+    MAP = 4;
+    OPTIONAL = 5;
+  }
+
+  // The data type of the element, identifies if the OptionalProto value
+  // is Tensor, Sparse Tensor, Sequence, Map, or Optional.
+  // The type of the optional value MUST match the elem_type specified.
+  // This field MUST have a valid OptionalProto.DataType value.
+  optional int32 elem_type = 2;
+
+  // For TensorProto value.
+  // When this field is present, the elem_type field MUST be TENSOR.
+  optional TensorProto tensor_value = 3;
+
+  // For SparseTensorProto value.
+  // When this field is present, the elem_type field MUST be SPARSE_TENSOR.
+  optional SparseTensorProto sparse_tensor_value = 4;
+
+  // For SequenceProto value.
+  // When this field is present, the elem_type field MUST be SEQUENCE.
+  optional SequenceProto sequence_value = 5;
+
+  // For MapProto value.
+  // When this field is present, the elem_type field MUST be MAP.
+  optional MapProto map_value = 6;
+
+  // For OptionalProto value, allowing optional to be of itself (completeness)
+  // When this field is present, the elem_type field MUST be OPTIONAL.
+  optional OptionalProto optional_value = 7;
+
+}
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-ml.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-ml.proto
new file mode 100644
index 0000000000000000000000000000000000000000..a6fd805587d1e45d4b25b439d46ed4b98260263e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-ml.proto
@@ -0,0 +1,1002 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// SPDX-License-Identifier: Apache-2.0
+
+
+syntax = "proto2";
+
+package onnx;
+
+// Overview
+//
+// ONNX is an open specification that is comprised of the following components:
+//
+// 1)  A definition of an extensible computation graph model.
+// 2)  Definitions of standard data types.
+// 3)  Definitions of built-in operators.
+//
+// This document describes the syntax of models and their computation graphs,
+// as well as the standard data types. Together, they are referred to as the ONNX
+// Intermediate Representation, or 'IR' for short.
+//
+// The normative semantic specification of the ONNX IR is found in docs/IR.md.
+// Definitions of the built-in neural network operators may be found in docs/Operators.md.
+// Definitions of the built-in classical machine learning operators may be found in
+// docs/Operators-ml.md.
+
+// Notes
+//
+// Protobuf compatibility
+//
+// To simplify framework compatibility, ONNX is defined using the subset of protobuf
+// that is compatible with both protobuf v2 and v3. This means that we do not use any
+// protobuf features that are only available in one of the two versions.
+//
+// Here are the most notable contortions we have to carry out to work around
+// these limitations:
+//
+//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
+//     of key-value pairs, where order does not matter and duplicates
+//     are not allowed.
+
+
+// Versioning
+//
+// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
+//
+// To be compatible with both proto2 and proto3, we will use a version number
+// that is not defined by the default value but an explicit enum number.
+enum Version {
+  // proto3 requires the first enum value to be zero.
+  // We add this just to appease the compiler.
+  _START_VERSION = 0;
+  // The version field is always serialized and we will use it to store the
+  // version that the  graph is generated from. This helps us set up version
+  // control.
+  // For the IR, we are using simple numbers starting with 0x00000001,
+  // which was the version we published on Oct 10, 2017.
+  IR_VERSION_2017_10_10 = 0x0000000000000001;
+
+  // IR_VERSION 2 published on Oct 30, 2017
+  // - Added type discriminator to AttributeProto to support proto3 users
+  IR_VERSION_2017_10_30 = 0x0000000000000002;
+
+  // IR VERSION 3 published on Nov 3, 2017
+  // - For operator versioning:
+  //    - Added new message OperatorSetIdProto
+  //    - Added opset_import in ModelProto
+  // - For vendor extensions, added domain in NodeProto
+  IR_VERSION_2017_11_3 = 0x0000000000000003;
+
+  // IR VERSION 4 published on Jan 22, 2019
+  // - Relax constraint that initializers should be a subset of graph inputs
+  // - Add type BFLOAT16
+  IR_VERSION_2019_1_22 = 0x0000000000000004;
+
+  // IR VERSION 5 published on March 18, 2019
+  // - Add message TensorAnnotation.
+  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
+  IR_VERSION_2019_3_18 = 0x0000000000000005;
+
+  // IR VERSION 6 published on Sep 19, 2019
+  // - Add support for sparse tensor constants stored in model.
+  //   - Add message SparseTensorProto
+  //   - Add sparse initializers
+  IR_VERSION_2019_9_19 = 0x0000000000000006;
+
+  // IR VERSION 7 published on May 8, 2020
+  // - Add support to allow function body graph to rely on multiple external operator sets.
+  // - Add a list to promote inference graph's initializers to global and
+  //   mutable variables. Global variables are visible in all graphs of the
+  //   stored models.
+  // - Add message TrainingInfoProto to store initialization
+  //   method and training algorithm. The execution of TrainingInfoProto
+  //   can modify the values of mutable variables.
+  // - Implicitly add inference graph into each TrainingInfoProto's algorithm.
+  IR_VERSION_2020_5_8 = 0x0000000000000007;
+
+  // IR VERSION 8 published on July 30, 2021
+  // Introduce TypeProto.SparseTensor
+  // Introduce TypeProto.Optional
+  // Added a list of FunctionProtos local to the model
+  // Deprecated since_version and operator status from FunctionProto
+  IR_VERSION_2021_7_30 = 0x0000000000000008;
+
+  // IR VERSION 9 published on May 5, 2023
+  // Added AttributeProto to FunctionProto so that default attribute values can be set.
+  // Added FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ.
+  IR_VERSION_2023_5_5 = 0x0000000000000009;
+
+  // IR VERSION 10 published on March 25, 2024
+  // Added UINT4, INT4, overload field for functions and metadata_props on multiple proto definitions.
+  IR_VERSION_2024_3_25 = 0x000000000000000A;
+
+  // IR VERSION 11 published on May 12, 2025
+  // Added FLOAT4E2M1, multi-device protobuf classes.
+  IR_VERSION_2025_05_12 = 0x000000000000000B;
+
+  // IR VERSION 12 published on TBD
+  // Added FLOAT8E8M0.
+  IR_VERSION = 0x000000000000000C;
+}
+
+// Attributes
+//
+// A named attribute containing either singular float, integer, string, graph,
+// and tensor values, or repeated float, integer, string, graph, and tensor values.
+// An AttributeProto MUST contain the name field, and *only one* of the
+// following content fields, effectively enforcing a C/C++ union equivalent.
+message AttributeProto {
+  reserved 12, 16 to 19;
+  reserved "v";
+
+  // Note: this enum is structurally identical to the OpSchema::AttrType
+  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
+  enum AttributeType {
+    UNDEFINED = 0;
+    FLOAT = 1;
+    INT = 2;
+    STRING = 3;
+    TENSOR = 4;
+    GRAPH = 5;
+    SPARSE_TENSOR = 11;
+    TYPE_PROTO = 13;
+
+    FLOATS = 6;
+    INTS = 7;
+    STRINGS = 8;
+    TENSORS = 9;
+    GRAPHS = 10;
+    SPARSE_TENSORS = 12;
+    TYPE_PROTOS = 14;
+  }
+
+  // The name field MUST be present for this version of the IR.
+  optional string name = 1;           // namespace Attribute
+
+  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
+  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
+  // in parent scope.
+  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
+  optional string ref_attr_name = 21;
+
+  // A human-readable documentation for this attribute. Markdown is allowed.
+  optional string doc_string = 13;
+
+  // The type field MUST be present for this version of the IR.
+  // For 0.0.1 versions of the IR, this field was not defined, and
+  // implementations needed to use has_field heuristics to determine
+  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
+  // field MUST be set and match the f|i|s|t|... field in use.  This
+  // change was made to accommodate proto3 implementations.
+  optional AttributeType type = 20;   // discriminator that indicates which field below is in use
+
+  // Exactly ONE of the following fields must be present for this version of the IR
+  optional float f = 2;               // float
+  optional int64 i = 3;               // int
+  optional bytes s = 4;               // UTF-8 string
+  optional TensorProto t = 5;         // tensor value
+  optional GraphProto g = 6;          // graph
+  optional SparseTensorProto sparse_tensor = 22;  // sparse tensor value
+  // Do not use field below, it's deprecated.
+  // optional ValueProto v = 12;         // value - subsumes everything but graph
+  optional TypeProto tp = 14;          // type proto
+
+  repeated float floats = 7;          // list of floats
+  repeated int64 ints = 8;            // list of ints
+  repeated bytes strings = 9;         // list of UTF-8 strings
+  repeated TensorProto tensors = 10;  // list of tensors
+  repeated GraphProto graphs = 11;    // list of graph
+  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
+  repeated TypeProto type_protos = 15;// list of type protos
+}
+
+// Defines information on value, including the name, the type, and
+// the shape of the value.
+message ValueInfoProto {
+  // This field MUST be present in this version of the IR.
+  optional string name = 1;     // namespace Value
+  // This field MUST be present in this version of the IR for
+  // inputs and outputs of the top-level graph.
+  optional TypeProto type = 2;
+  // A human-readable documentation for this value. Markdown is allowed.
+  optional string doc_string = 3;
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 4;
+}
+
+// Nodes
+//
+// Computation graphs are made up of a DAG of nodes, which represent what is
+// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
+//
+// For example, it can be a node of type "Conv" that takes in an image, a filter
+// tensor and a bias tensor, and produces the convolved output.
+message NodeProto {
+  repeated string input = 1;    // namespace Value
+  repeated string output = 2;   // namespace Value
+
+  // An optional identifier for this node in a graph.
+  // This field MAY be absent in this version of the IR.
+  optional string name = 3;     // namespace Node
+
+  // The symbolic identifier of the Operator to execute.
+  optional string op_type = 4;  // namespace Operator
+  // The domain of the OperatorSet that specifies the operator named by op_type.
+  optional string domain = 7;   // namespace Domain
+  // Overload identifier, used only to map this to a model-local function.
+  optional string overload = 8;
+
+  // Additional named attributes.
+  repeated AttributeProto attribute = 5;
+
+  // A human-readable documentation for this node. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 9;
+
+  // Configuration of multi-device annotations.
+  repeated NodeDeviceConfigurationProto device_configurations = 10;
+}
+
+// IntIntListEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message IntIntListEntryProto {
+  optional int64 key = 1;
+  repeated int64 value = 2;
+};
+
+// Multi-device configuration proto for NodeProto.
+message NodeDeviceConfigurationProto {
+    // This field MUST be present for this version of the IR.
+    // ID of the configuration. MUST match the name of a DeviceConfigurationProto.
+    optional string configuration_id = 1;
+    // Sharding spec for the node.
+    repeated ShardingSpecProto sharding_spec = 2;
+    // Pipeline stage of this node.
+    optional int32 pipeline_stage = 3;
+}
+
+// ShardingSpecProto: This describes the sharding spec for a specific
+// input or output tensor of a node.
+message ShardingSpecProto {
+  // This field MUST be present for this version of the IR.
+  // Identifies the input or output of the node that is being sharded.
+  // Required to match a name specified in the node's input or output list of ValueInfoProtos.
+  // It is called `logical tensor` in subsequent descriptions.
+  optional string tensor_name = 1;
+
+  // The following is the list of devices across which the logical
+  // tensor is sharded or replicated.
+  repeated int64 device = 2;
+
+  // Each element v in above field devices may represent either a
+  // device or a set of devices (when we want the same shard/tensor
+  // to be replicated across a subset of devices), as indicated by
+  // the following optional map. If the map contains an entry for v,
+  // then v represents a device group, and the map indicates the set
+  // of devices in that group.
+  repeated IntIntListEntryProto index_to_device_group_map = 3;
+
+  // The following is the sharded-shape of the tensor, consisting of
+  // the sharding-spec for each axis of the tensor.
+  repeated ShardedDimProto sharded_dim = 4;
+}
+
+// ShardedDimProto: This describes the sharding spec for a single
+// axis of a sharded tensor.
+message ShardedDimProto {
+  // This field MUST be present for this version of the IR.
+  // The axis this sharding corresponds to. Must be in the range of
+  // [-r, r - 1], where r is the rank of the tensor. Negative axis values means
+  // counting from the back.
+  optional int64 axis = 1;
+
+  // Describes how the tensor on the provided axis is sharded.
+  // The common-case is described by a single instance of SimpleShardedDimProto.
+  // Multiple instances can be used to handle cases where a sharded
+  // tensor is reshaped, fusing multiple axes into one.
+  repeated SimpleShardedDimProto simple_sharding = 2;
+}
+
+// SimpleShardedDimProto: Indicates that N blocks are divided into M shards.
+// N is allowed to be symbolic where M is required to be a constant.
+message SimpleShardedDimProto {
+    // Dimension value to be sharded.
+    oneof dim {
+        int64 dim_value = 1;
+        string dim_param = 2;
+    }
+
+    // This field MUST be present for this version of the IR.
+    // Number of shards to split dim into.
+    optional int64 num_shards = 3;
+}
+
+// Training information
+// TrainingInfoProto stores information for training a model.
+// In particular, this defines two functionalities: an initialization-step
+// and a training-algorithm-step. Initialization resets the model
+// back to its original state as if no training has been performed.
+// Training algorithm improves the model based on input data.
+//
+// The semantics of the initialization-step is that the initializers
+// in ModelProto.graph and in TrainingInfoProto.algorithm are first
+// initialized as specified by the initializers in the graph, and then
+// updated by the "initialization_binding" in every instance in
+// ModelProto.training_info.
+//
+// The field "algorithm" defines a computation graph which represents a
+// training algorithm's step. After the execution of a
+// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
+// may be immediately updated. If the targeted training algorithm contains
+// consecutive update steps (such as block coordinate descent methods),
+// the user needs to create a TrainingInfoProto for each step.
+message TrainingInfoProto {
+  // This field describes a graph to compute the initial tensors
+  // upon starting the training process. Initialization graph has no input
+  // and can have multiple outputs. Usually, trainable tensors in neural
+  // networks are randomly initialized. To achieve that, for each tensor,
+  // the user can put a random number operator such as RandomNormal or
+  // RandomUniform in TrainingInfoProto.initialization.node and assign its
+  // random output to the specific tensor using "initialization_binding".
+  // This graph can also set the initializers in "algorithm" in the same
+  // TrainingInfoProto; a use case is resetting the number of training
+  // iteration to zero.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Thus, no initializer would be changed by default.
+  optional GraphProto initialization = 1;
+
+  // This field represents a training algorithm step. Given required inputs,
+  // it computes outputs to update initializers in its own or inference graph's
+  // initializer lists. In general, this field contains loss node, gradient node,
+  // optimizer node, increment of iteration count.
+  //
+  // An execution of the training algorithm step is performed by executing the
+  // graph obtained by combining the inference graph (namely "ModelProto.graph")
+  // and the "algorithm" graph. That is, the actual
+  // input/initializer/output/node/value_info/sparse_initializer list of
+  // the training graph is the concatenation of
+  // "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
+  // and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
+  // in that order. This combined graph must satisfy the normal ONNX conditions.
+  // Now, let's provide a visualization of graph combination for clarity.
+  // Let the inference graph (i.e., "ModelProto.graph") be
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
+  // and the "algorithm" graph be
+  //    tensor_d -> Add -> tensor_e
+  // The combination process results
+  //    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
+  //
+  // Notice that an input of a node in the "algorithm" graph may reference the
+  // output of a node in the inference graph (but not the other way round). Also, inference
+  // node cannot reference inputs of "algorithm". With these restrictions, inference graph
+  // can always be run independently without training information.
+  //
+  // By default, this field is an empty graph and its evaluation does not
+  // produce any output. Evaluating the default training step never
+  // update any initializers.
+  optional GraphProto algorithm = 2;
+
+  // This field specifies the bindings from the outputs of "initialization" to
+  // some initializers in "ModelProto.graph.initializer" and
+  // the "algorithm.initializer" in the same TrainingInfoProto.
+  // See "update_binding" below for details.
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "initialization".
+  repeated StringStringEntryProto initialization_binding = 3;
+
+  // Gradient-based training is usually an iterative procedure. In one gradient
+  // descent iteration, we apply
+  //
+  // x = x - r * g
+  //
+  // where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
+  // gradient of "x" with respect to a chosen loss. To avoid adding assignments
+  // into the training graph, we split the update equation into
+  //
+  // y = x - r * g
+  // x = y
+  //
+  // The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
+  // tell that "y" should be assigned to "x", the field "update_binding" may
+  // contain a key-value pair of strings, "x" (key of StringStringEntryProto)
+  // and "y" (value of StringStringEntryProto).
+  // For a neural network with multiple trainable (mutable) tensors, there can
+  // be multiple key-value pairs in "update_binding".
+  //
+  // The initializers appears as keys in "update_binding" are considered
+  // mutable variables. This implies some behaviors
+  // as described below.
+  //
+  //  1. We have only unique keys in all "update_binding"s so that two
+  //     variables may not have the same name. This ensures that one
+  //     variable is assigned up to once.
+  //  2. The keys must appear in names of "ModelProto.graph.initializer" or
+  //     "TrainingInfoProto.algorithm.initializer".
+  //  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
+  //  4. Mutable variables are initialized to the value specified by the
+  //     corresponding initializer, and then potentially updated by
+  //     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
+  //
+  // This field usually contains names of trainable tensors
+  // (in ModelProto.graph), optimizer states such as momentums in advanced
+  // stochastic gradient methods (in TrainingInfoProto.graph),
+  // and number of training iterations (in TrainingInfoProto.graph).
+  //
+  // By default, this field is empty and no initializer would be changed
+  // by the execution of "algorithm".
+  repeated StringStringEntryProto update_binding = 4;
+}
+
+// Models
+//
+// ModelProto is a top-level file/container format for bundling a ML model and
+// associating its computation graph with metadata.
+//
+// The semantics of the model are described by the associated GraphProto's.
+message ModelProto {
+  // The version of the IR this model targets. See Version enum above.
+  // This field MUST be present.
+  optional int64 ir_version = 1;
+
+  // The OperatorSets this model relies on.
+  // All ModelProtos MUST have at least one entry that
+  // specifies which version of the ONNX OperatorSet is
+  // being imported.
+  //
+  // All nodes in the ModelProto's graph will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets.
+  repeated OperatorSetIdProto opset_import = 8;
+
+  // The name of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_name = 2;
+
+  // The version of the framework or tool used to generate this model.
+  // This field SHOULD be present to indicate which implementation/tool/framework
+  // emitted the model.
+  optional string producer_version = 3;
+
+  // Domain name of the model.
+  // We use reverse domain names as name space indicators. For example:
+  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
+  //
+  // Together with `model_version` and GraphProto.name, this forms the unique identity of
+  // the graph.
+  optional string domain = 4;
+
+  // The version of the graph encoded. See Version enum below.
+  optional int64 model_version = 5;
+
+  // A human-readable documentation for this model. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The parameterized graph that is evaluated to execute the model.
+  optional GraphProto graph = 7;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+
+  // Training-specific information. Sequentially executing all stored
+  // `TrainingInfoProto.algorithm`s and assigning their outputs following
+  // the corresponding `TrainingInfoProto.update_binding`s is one training
+  // iteration. Similarly, to initialize the model
+  // (as if training hasn't happened), the user should sequentially execute
+  // all stored `TrainingInfoProto.initialization`s and assigns their outputs
+  // using `TrainingInfoProto.initialization_binding`s.
+  //
+  // If this field is empty, the training behavior of the model is undefined.
+  repeated TrainingInfoProto training_info = 20;
+
+  // A list of function protos local to the model.
+  //
+  // The (domain, name, overload) tuple must be unique across the function protos in this list.
+  // In case of any conflicts the behavior (whether the model local functions are given higher priority,
+  // or standard operator sets are given higher priority or this is treated as error) is defined by
+  // the runtimes.
+  //
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto and other model local FunctionProtos.
+  // Example, if same operator set say 'A' is imported by a FunctionProto and ModelProto
+  // or by 2 FunctionProtos then versions for the operator set may be different but,
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same for every node in the function body.
+  //
+  // One FunctionProto can reference other FunctionProto in the model, however, recursive reference
+  // is not allowed.
+  repeated FunctionProto functions = 25;
+
+  // Describes different target configurations for a multi-device use case.
+  // A model MAY describe multiple multi-device configurations for execution.
+  repeated DeviceConfigurationProto configuration = 26;
+};
+
+// DeviceConfigurationProto describes a multi-device configuration for a model.
+message DeviceConfigurationProto {
+    // This field MUST be present for this version of the IR.
+    // Name of the configuration.
+    optional string name = 1;
+    // This field MUST be present for this version of the IR.
+    // Number of devices inside this configuration.
+    optional int32 num_devices = 2;
+    // Optional names of the devices. MUST be length of num_devices if provided.
+    repeated string device = 3;
+}
+
+// StringStringEntryProto follows the pattern for cross-proto-version maps.
+// See https://developers.google.com/protocol-buffers/docs/proto3#maps
+message StringStringEntryProto {
+  optional string key = 1;
+  optional string value = 2;
+};
+
+message TensorAnnotation {
+  optional string tensor_name = 1;
+  //  pairs to annotate tensor specified by  above.
+  // The keys used in the mapping below must be pre-defined in ONNX spec.
+  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
+  // quantization parameter keys.
+  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
+}
+
+
+
+// Graphs
+//
+// A graph defines the computational logic of a model and is comprised of a parameterized
+// list of nodes that form a directed acyclic graph based on their inputs and outputs.
+// This is the equivalent of the "network" or "graph" in many deep learning
+// frameworks.
+message GraphProto {
+  // The nodes in the graph, sorted topologically.
+  repeated NodeProto node = 1;
+
+  // The name of the graph.
+  optional string name = 2;   // namespace Graph
+
+  // A list of named tensor values, used to specify constant inputs of the graph.
+  // Each initializer (both TensorProto as well SparseTensorProto) MUST have a name.
+  // The name MUST be unique across both initializer and sparse_initializer,
+  // but the name MAY also appear in the input list.
+  repeated TensorProto initializer = 5;
+
+  // Initializers (see above) stored in sparse format.
+  repeated SparseTensorProto sparse_initializer = 15;
+
+  // A human-readable documentation for this graph. Markdown is allowed.
+  optional string doc_string = 10;
+
+  // The inputs and outputs of the graph.
+  repeated ValueInfoProto input = 11;
+  repeated ValueInfoProto output = 12;
+
+  // Information for the values in the graph. The ValueInfoProto.name's
+  // must be distinct. It is optional for a value to appear in value_info list.
+  repeated ValueInfoProto value_info = 13;
+
+  // This field carries information to indicate the mapping among a tensor and its
+  // quantization parameter tensors. For example:
+  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
+  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
+  repeated TensorAnnotation quantization_annotation = 14;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 16;
+
+  reserved 3, 4, 6 to 9;
+  reserved "ir_version", "producer_version", "producer_tag", "domain";
+}
+
+// Tensors
+//
+// A serialized tensor value.
+message TensorProto {
+  enum DataType {
+    UNDEFINED = 0;
+    // Basic types.
+    FLOAT = 1;   // float
+    UINT8 = 2;   // uint8_t
+    INT8 = 3;    // int8_t
+    UINT16 = 4;  // uint16_t
+    INT16 = 5;   // int16_t
+    INT32 = 6;   // int32_t
+    INT64 = 7;   // int64_t
+    STRING = 8;  // string
+    BOOL = 9;    // bool
+
+    // IEEE754 half-precision floating-point format (16 bits wide).
+    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
+    FLOAT16 = 10;
+
+    DOUBLE = 11;
+    UINT32 = 12;
+    UINT64 = 13;
+    COMPLEX64 = 14;     // complex with float32 real and imaginary components
+    COMPLEX128 = 15;    // complex with float64 real and imaginary components
+
+    // Non-IEEE floating-point format based on IEEE754 single-precision
+    // floating-point number truncated to 16 bits.
+    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
+    BFLOAT16 = 16;
+
+    // Non-IEEE floating-point format based on papers
+    // FP8 Formats for Deep Learning, https://arxiv.org/abs/2209.05433,
+    // 8-bit Numerical Formats For Deep Neural Networks, https://arxiv.org/pdf/2206.02915.pdf.
+    // Operators supported FP8 are Cast, CastLike, QuantizeLinear, DequantizeLinear.
+    // The computation usually happens inside a block quantize / dequantize
+    // fused by the runtime.
+    FLOAT8E4M3FN = 17;    // float 8, mostly used for coefficients, supports nan, not inf
+    FLOAT8E4M3FNUZ = 18;  // float 8, mostly used for coefficients, supports nan, not inf, no negative zero
+    FLOAT8E5M2 = 19;      // follows IEEE 754, supports nan, inf, mostly used for gradients
+    FLOAT8E5M2FNUZ = 20;  // follows IEEE 754, supports nan, not inf, mostly used for gradients, no negative zero
+
+    // 4-bit integer data types
+    UINT4 = 21;  // Unsigned integer in range [0, 15]
+    INT4 = 22;   // Signed integer in range [-8, 7], using two's-complement representation
+
+    // 4-bit floating point data types
+    FLOAT4E2M1 = 23;
+
+    // E8M0 type used as the scale for microscaling (MX) formats:
+    // https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
+    FLOAT8E8M0 = 24;
+
+    // Future extensions go here.
+  }
+
+  // The shape of the tensor.
+  repeated int64 dims = 1;
+
+  // The data type of the tensor.
+  // This field MUST have a valid TensorProto.DataType value
+  optional int32 data_type = 2;
+
+  // For very large tensors, we may want to store them in chunks, in which
+  // case the following fields will specify the segment that is stored in
+  // the current TensorProto.
+  message Segment {
+    optional int64 begin = 1;
+    optional int64 end = 2;
+  }
+  optional Segment segment = 3;
+
+  // Tensor content must be organized in row-major order.
+  //
+  // Depending on the data_type field, exactly one of the fields below with
+  // name ending in _data is used to store the elements of the tensor.
+
+  // For float and complex64 values
+  // Complex64 tensors are encoded as a single array of floats,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
+  repeated float float_data = 4 [packed = true];
+
+  // For int32, uint8, int8, uint16, int16, uint4, int4, bool, (b)float16, float8, and float4:
+  // - (b)float16 and float8 values MUST be converted bit-wise into an unsigned integer
+  //   representation before being written to the buffer.
+  // - Each pair of uint4, int4, and float4 values MUST be packed as two 4-bit elements into a single byte.
+  //   The first element is stored in the 4 least significant bits (LSB),
+  //   and the second element is stored in the 4 most significant bits (MSB).
+  //
+  // Consequently:
+  // - For data types with a bit-width of 8 or greater, each `int32_data` stores one element.
+  // - For 4-bit data types, each `int32_data` stores two elements.
+  //
+  // When this field is present, the data_type field MUST be
+  // INT32, INT16, INT8, INT4, UINT16, UINT8, UINT4, BOOL, FLOAT16, BFLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, FLOAT8E8M0, FLOAT4E2M1
+  repeated int32 int32_data = 5 [packed = true];
+
+  // For strings.
+  // Each element of string_data is a UTF-8 encoded Unicode
+  // string. No trailing null, no leading BOM. The protobuf "string"
+  // scalar type is not used to match ML community conventions.
+  // When this field is present, the data_type field MUST be STRING
+  repeated bytes string_data = 6;
+
+  // For int64.
+  // When this field is present, the data_type field MUST be INT64
+  repeated int64 int64_data = 7 [packed = true];
+
+  // Optionally, a name for the tensor.
+  optional string name = 8; // namespace Value
+
+  // A human-readable documentation for this tensor. Markdown is allowed.
+  optional string doc_string = 12;
+
+  // Serializations can either use one of the fields above, or use this
+  // raw bytes field. The only exception is the string case, where one is
+  // required to store the content in the repeated bytes string_data field.
+  //
+  // When this raw_data field is used to store tensor value, elements MUST
+  // be stored in as fixed-width, little-endian order.
+  // Floating-point data types MUST be stored in IEEE 754 format.
+  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
+  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
+  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
+  // uint4 and int4 values must be packed to 4bitx2, the first element is stored in the 4 LSB and the second element is stored in the 4 MSB.
+  //
+  // Note: the advantage of specific field rather than the raw_data field is
+  // that in some cases (e.g. int data), protobuf does a better packing via
+  // variable length storage, and may lead to smaller binary footprint.
+  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
+  optional bytes raw_data = 9;
+
+  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
+  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
+  // external_data stores key-value pairs describing data location. Recognized keys are:
+  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
+  //                           protobuf model was stored
+  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
+  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
+  // - "length" (optional) - number of bytes containing data. Integer stored as string.
+  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
+  repeated StringStringEntryProto external_data = 13;
+
+  // Location of the data for this tensor. MUST be one of:
+  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
+  // - EXTERNAL - data stored in an external location as described by external_data field.
+  enum DataLocation {
+    DEFAULT = 0;
+    EXTERNAL = 1;
+  }
+
+  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
+  optional DataLocation data_location = 14;
+
+  // For double
+  // Complex128 tensors are encoded as a single array of doubles,
+  // with the real components appearing in odd numbered positions,
+  // and the corresponding imaginary component appearing in the
+  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
+  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
+  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
+  repeated double double_data = 10 [packed = true];
+
+  // For uint64 and uint32 values
+  // When this field is present, the data_type field MUST be
+  // UINT32 or UINT64
+  repeated uint64 uint64_data = 11 [packed = true];
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 16;
+}
+
+// A serialized sparse-tensor value
+message SparseTensorProto {
+  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
+  // The default-value is zero for numeric tensors, and empty-string for string tensors.
+  // values must have a non-empty name present which serves as a name for SparseTensorProto
+  // when used in sparse_initializer list.
+  optional TensorProto values = 1;
+
+  // The indices of the non-default values, which may be stored in one of two formats.
+  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
+  // corresponding to the j-th index of the i-th value (in the values tensor).
+  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
+  // must be the linearized-index of the i-th value (in the values tensor).
+  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
+  // using the shape provided below.
+  // The indices must appear in ascending order without duplication.
+  // In the first format, the ordering is lexicographic-ordering:
+  // e.g., index-value [1,4] must appear before [2,1]
+  optional TensorProto indices = 2;
+
+  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
+  repeated int64 dims = 3;
+}
+
+// Defines a tensor shape. A dimension can be either an integer value
+// or a symbolic variable. A symbolic variable represents an unknown
+// dimension.
+message TensorShapeProto {
+  message Dimension {
+    oneof value {
+      int64 dim_value = 1;
+      string dim_param = 2;   // namespace Shape
+    };
+    // Standard denotation can optionally be used to denote tensor
+    // dimensions with standard semantic descriptions to ensure
+    // that operations are applied to the correct axis of a tensor.
+    // Refer to https://github.com/onnx/onnx/blob/main/docs/DimensionDenotation.md#denotation-definition
+    // for pre-defined dimension denotations.
+    optional string denotation = 3;
+  };
+  repeated Dimension dim = 1;
+}
+
+// Types
+//
+// The standard ONNX data types.
+message TypeProto {
+
+  message Tensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+  // repeated T
+  message Sequence {
+    // The type and optional shape of each element of the sequence.
+    // This field MUST be present for this version of the IR.
+    optional TypeProto elem_type = 1;
+  };
+
+  // map
+  message Map {
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
+    optional int32 key_type = 1;
+    // This field MUST be present for this version of the IR.
+    optional TypeProto value_type = 2;
+  };
+
+  // wrapper for Tensor, Sequence, or Map
+  message Optional {
+    // The type and optional shape of the element wrapped.
+    // This field MUST be present for this version of the IR.
+    // Possible values correspond to OptionalProto.DataType enum
+    optional TypeProto elem_type = 1;
+  };
+
+
+  message SparseTensor {
+    // This field MUST NOT have the value of UNDEFINED
+    // This field MUST have a valid TensorProto.DataType value
+    // This field MUST be present for this version of the IR.
+    optional int32 elem_type = 1;
+    optional TensorShapeProto shape = 2;
+  }
+
+
+  message Opaque {
+    // When missing, the domain is the same as the model's.
+    optional string domain = 1;
+    // The name is optional but significant when provided.
+    optional string name = 2;
+    // parameters that help defining the type
+    // DEPRECATED do not use.
+    // repeated TypeProto parameters = 3;
+  }
+
+
+  oneof value {
+    // The type of a tensor.
+    Tensor tensor_type = 1;
+
+    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
+    //        as input and output to graphs and nodes. These types are needed to naturally
+    //        support classical ML operators.  DNN operators SHOULD restrict their input
+    //        and output types to tensors.
+
+    // The type of a sequence.
+    Sequence sequence_type = 4;
+
+    // The type of a map.
+    Map map_type = 5;
+
+    // The type of an optional.
+    Optional optional_type = 9;
+
+
+    // Type of the sparse tensor
+    SparseTensor sparse_tensor_type = 8;
+
+
+    Opaque opaque_type = 7;
+
+  }
+
+  // An optional denotation can be used to denote the whole
+  // type with a standard semantic description as to what is
+  // stored inside. Refer to https://github.com/onnx/onnx/blob/main/docs/TypeDenotation.md#type-denotation-definition
+  // for pre-defined type denotations.
+  optional string denotation = 6;
+}
+
+// Operator Sets
+//
+// OperatorSets are uniquely identified by a (domain, opset_version) pair.
+message OperatorSetIdProto {
+  // The domain of the operator set being identified.
+  // The empty string ("") or absence of this field implies the operator
+  // set that is defined as part of the ONNX specification.
+  // This field MUST be present in this version of the IR when referring to any other operator set.
+  optional string domain = 1;
+
+  // The version of the operator set being identified.
+  // This field MUST be present in this version of the IR.
+  optional int64 version = 2;
+}
+
+// Operator/function status.
+enum OperatorStatus {
+    EXPERIMENTAL = 0;
+    STABLE = 1;
+}
+
+message FunctionProto {
+  // The name of the function, similar to op_type in NodeProto.
+  // This is part of the unique-id (domain, name, overload) of FunctionProtos in a model.
+  optional string name = 1;
+
+  // Deprecated since IR Version 8
+  // optional int64 since_version = 2;
+  reserved 2;
+  reserved "since_version";
+
+  // Deprecated since IR Version 8
+  // optional OperatorStatus status = 3;
+  reserved 3;
+  reserved "status";
+
+  // The inputs and outputs of the function.
+  repeated string input = 4;
+  repeated string output = 5;
+
+  // The attribute parameters of the function.
+  // It is for function parameters without default values.
+  repeated string attribute = 6;
+
+  // The attribute protos of the function.
+  // It is for function attributes with default values.
+  // A function attribute shall be represented either as
+  // a string attribute or an AttributeProto, not both.
+  repeated AttributeProto attribute_proto = 11;
+
+  // The nodes in the function.
+  repeated NodeProto node = 7;
+  // A human-readable documentation for this function. Markdown is allowed.
+  optional string doc_string = 8;
+
+  // The OperatorSets this function body (graph) relies on.
+  //
+  // All nodes in the function body (graph) will bind against the operator
+  // with the same-domain/same-op_type operator with the HIGHEST version
+  // in the referenced operator sets. This means at most one version can be relied
+  // for one domain.
+  //
+  // The operator sets imported by FunctionProto should be compatible with the ones
+  // imported by ModelProto. Example, if same operator set say 'A' is imported by FunctionProto
+  // and ModelProto then versions for the operator set may be different but,
+  // the operator schema returned for op_type, domain, version combination
+  // for both the versions should be same.
+
+  repeated OperatorSetIdProto opset_import = 9;
+
+  // The domain which this function belongs to.
+  // This is part of the unique-id (domain, name, overload) of FunctionProtos in a model.
+  optional string domain = 10;
+
+  // The overload identifier of the function.
+  // This is part of the unique-id (domain, name, overload) of FunctionProtos in a model.
+  optional string overload = 13;
+
+  // Information for the values in the function. The ValueInfoProto.name's
+  // must be distinct and refer to names in the function (including inputs,
+  // outputs, and intermediate values). It is optional for a value to appear
+  // in value_info list.
+  repeated ValueInfoProto value_info = 12;
+
+  // Named metadata values; keys should be distinct.
+  repeated StringStringEntryProto metadata_props = 14;
+}
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators-ml.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators-ml.proto
new file mode 100644
index 0000000000000000000000000000000000000000..a9a07f12aa4f0ebd0999926449cae7886c6e231b
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators-ml.proto
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the Apache-2.0 license.
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx-ml.proto";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distinguished byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.in.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.in.proto
new file mode 100644
index 0000000000000000000000000000000000000000..cc84dd2f900be8aeeb8b235add083e3e9b44d98f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.in.proto
@@ -0,0 +1,131 @@
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the Apache-2.0 license.
+
+syntax = "proto2";
+
+package {PACKAGE_NAME};
+// #if ONNX-ML
+import "onnx/onnx-ml.proto";
+// #else
+import "onnx/onnx.proto";
+// #endif
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distinguished byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.proto b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.proto
new file mode 100644
index 0000000000000000000000000000000000000000..2f772c64ce340bd91b825acde3fbaa4a8af2f0f3
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators.proto
@@ -0,0 +1,136 @@
+//
+// WARNING: This file is automatically generated!  Please edit onnx.in.proto.
+//
+
+
+// Copyright (c) ONNX Project Contributors.
+// Licensed under the Apache-2.0 license.
+
+syntax = "proto2";
+
+package onnx;
+import "onnx/onnx.proto";
+
+//
+// This file contains the proto definitions for OperatorSetProto and
+// OperatorProto.  OperatorSetProtos are used to describe a versioned
+// set of operators that can be used by a ModelProto.
+//
+// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
+// format, however their usage is different.
+//
+// ModelProto files are used to describe executable graphs that can be
+// executed directly by a framework, runtime, or engine.
+//
+// OperatorSetProto files are used to describe a set of operators that are
+// available in a given environment.  The file TBD.TBD is the OperatorSetProto
+// that describes the ONNX standard operators.
+//
+
+// An OperatorProto represents the immutable specification of the signature
+// and semantics of an operator.
+//
+// Operators are declared as part of an OperatorSet, which also defines the
+// domain name for the set.
+//
+// Operators are uniquely identified by a three part identifier
+//   (domain, op_type, since_version)
+// where
+//   *domain* is the domain of an operator set that
+//      contains this operator specification.
+//
+//   *op_type* is the name of the operator as referenced by a
+//      NodeProto.op_type
+//
+//   *since_version* is the version of the operator set that
+//      this operator was initially declared in.
+//
+message OperatorProto {
+  // The name of the operator within a domain.
+  // This field MUST be present in this version of the IR.
+  optional string op_type = 1;
+
+  // The version of the operator set that first introduced this
+  // operator. This value MUST be the same value as the
+  // opset_version of the operator set that first published this operator.
+  // Subsequent versions of the operator set MUST NOT alter the signature
+  // or semantics of the operator once published as STABLE.
+  // This field MUST be present in this version of the IR.
+  optional int64 since_version = 2;
+
+  // This field indicates whether the syntax, semantics, or presence
+  // of this operator is in an experimental or stable stage. Once an
+  // operator is published as STABLE, it's syntax and semantics MUST NOT
+  // change in subsequent versions of the operator set.
+  // When an operator is published as EXPERIMENTAL, the syntax and semantics
+  // of the operator MAY change across operator set versions.
+  // Operators "become" stable by deprecating the experimental version and
+  // introducing a new stable operator with the same op_type.
+  optional OperatorStatus status = 3;
+
+  // Eventually we will declare the signature of the operator here
+
+  // A human-readable documentation for this operator. Markdown is allowed.
+  optional string doc_string = 10;
+}
+
+// An OperatorSetProto represents an immutable set of immutable operator
+// specifications.
+//
+// The domain of the set (OperatorSetProto.domain) is a reverse-DNS name
+// that disambiguates operator sets defined by independent entities.
+//
+// The version of the set (opset_version) is a monotonically increasing
+// integer that indicates changes to the membership of the operator set.
+//
+//
+// Operator sets are uniquely identified by a two part identifier (domain, opset_version)
+//
+// Like ModelProto, OperatorSetProto is intended as a top-level file/wire format,
+// and thus has the standard format headers in addition to the operator set information.
+//
+message OperatorSetProto {
+  // All OperatorSetProtos start with a distinguished byte sequence to disambiguate
+  // protobuf files containing OperatorSets from other content.
+  // This field MUST be "ONNXOPSET"
+  // This field MUST be present in this version of the IR
+  optional string magic = 1;
+
+  // All OperatorSetProtos indicate the version of the IR syntax and semantics
+  // they adhere to. It is always IR_VERSION.
+  // This field MUST be present in this version of the IR
+  optional int64 ir_version = 2;
+
+  // The prerelease component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_version_prerelease = 3;
+
+  // The build metadata component of the SemVer of the IR.
+  // This field MAY be absent in this version of the IR
+  optional string ir_build_metadata = 7;
+
+  // Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
+  optional string domain = 4;
+
+  // The version of the set of operators. This is a simple int value
+  // that is monotonically increasing as new versions of the operator set
+  // are published. All operators in this set MUST have since_version
+  // <= opset_version.
+  optional int64 opset_version = 5;
+
+  // A human-readable documentation for this set of operators. Markdown is allowed.
+  optional string doc_string = 6;
+
+  // The operators specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos in operator
+  repeated OperatorProto operator = 8;
+
+  // The functions specified by this operator set.
+  // The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
+  repeated FunctionProto functions = 9;
+}
+
+
+// For using protobuf-lite
+option optimize_for = LITE_RUNTIME;
+
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators_pb.h b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators_pb.h
new file mode 100644
index 0000000000000000000000000000000000000000..05cf569283fb5d60ec66d0fa00d7950658f71985
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/onnx-operators_pb.h
@@ -0,0 +1,12 @@
+/*
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "onnx/onnx_pb.h"
+#ifdef ONNX_ML
+#include "onnx/onnx-operators-ml.pb.h"
+#else
+#include "onnx/onnx-operators.pb.h"
+#endif
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/py.typed b/pythonProject/.venv/Lib/site-packages/onnx/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/py_utils.h b/pythonProject/.venv/Lib/site-packages/onnx/py_utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..250bdd6fce18eddc031b0f0dbcf130ed35d27513
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/py_utils.h
@@ -0,0 +1,23 @@
+// Copyright (c) ONNX Project Contributors
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include 
+
+#include "onnx/proto_utils.h"
+
+namespace ONNX_NAMESPACE {
+namespace py = pybind11;
+
+template 
+bool ParseProtoFromPyBytes(Proto* proto, const py::bytes& bytes) {
+  // Get the buffer from Python bytes object
+  char* buffer = nullptr;
+  Py_ssize_t length = 0;
+  PyBytes_AsStringAndSize(bytes.ptr(), &buffer, &length);
+
+  return ParseProtoFromBytes(proto, buffer, length);
+}
+} // namespace ONNX_NAMESPACE
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/serialization.py b/pythonProject/.venv/Lib/site-packages/onnx/serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..384ea259430d31671f857ba853d594d497483061
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/serialization.py
@@ -0,0 +1,212 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import warnings
+
+__all__ = [
+    "registry",
+]
+
+import typing
+from typing import Any, Optional, Protocol, TypeVar
+
+import google.protobuf.json_format
+import google.protobuf.message
+import google.protobuf.text_format
+
+import onnx
+
+if typing.TYPE_CHECKING:
+    from collections.abc import Collection
+
+_Proto = TypeVar("_Proto", bound=google.protobuf.message.Message)
+# Encoding used for serializing and deserializing text files
+_ENCODING = "utf-8"
+
+
+class ProtoSerializer(Protocol):
+    """A serializer-deserializer to and from in-memory Protocol Buffers representations."""
+
+    # Format supported by the serializer. E.g. "protobuf"
+    supported_format: str
+    # File extensions supported by the serializer. E.g. frozenset({".onnx", ".pb"})
+    # Be careful to include the dot in the file extension.
+    file_extensions: Collection[str]
+
+    # NOTE: The methods defined are serialize_proto and deserialize_proto and not the
+    # more generic serialize and deserialize to leave space for future protocols
+    # that are defined to serialize/deserialize the ONNX in memory IR.
+    # This way a class can implement both protocols.
+
+    def serialize_proto(self, proto: _Proto) -> Any:
+        """Serialize a in-memory proto to a serialized data type."""
+
+    def deserialize_proto(self, serialized: Any, proto: _Proto) -> _Proto:
+        """Parse a serialized data type into a in-memory proto."""
+
+
+class _Registry:
+    def __init__(self) -> None:
+        self._serializers: dict[str, ProtoSerializer] = {}
+        # A mapping from file extension to format
+        self._extension_to_format: dict[str, str] = {}
+
+    def register(self, serializer: ProtoSerializer) -> None:
+        self._serializers[serializer.supported_format] = serializer
+        self._extension_to_format.update(
+            dict.fromkeys(serializer.file_extensions, serializer.supported_format)
+        )
+
+    def get(self, fmt: str) -> ProtoSerializer:
+        """Get a serializer for a format.
+
+        Args:
+            fmt: The format to get a serializer for.
+
+        Returns:
+            ProtoSerializer: The serializer for the format.
+
+        Raises:
+            ValueError: If the format is not supported.
+        """
+        try:
+            return self._serializers[fmt]
+        except KeyError:
+            raise ValueError(
+                f"Unsupported format: '{fmt}'. Supported formats are: {self._serializers.keys()}"
+            ) from None
+
+    def get_format_from_file_extension(self, file_extension: str) -> str | None:
+        """Get the corresponding format from a file extension.
+
+        Args:
+            file_extension: The file extension to get a format for.
+
+        Returns:
+            The format for the file extension, or None if not found.
+        """
+        return self._extension_to_format.get(file_extension)
+
+
+class _ProtobufSerializer(ProtoSerializer):
+    """Serialize and deserialize protobuf message."""
+
+    supported_format = "protobuf"
+    file_extensions = frozenset({".onnx", ".pb"})
+
+    def serialize_proto(self, proto: _Proto) -> bytes:
+        if hasattr(proto, "SerializeToString") and callable(proto.SerializeToString):
+            try:
+                result = proto.SerializeToString()
+            except ValueError as e:
+                if proto.ByteSize() >= onnx.checker.MAXIMUM_PROTOBUF:
+                    raise ValueError(
+                        "The proto size is larger than the 2 GB limit. "
+                        "Please use save_as_external_data to save tensors separately from the model file."
+                    ) from e
+                raise
+            return result  # type: ignore
+        raise TypeError(
+            f"No SerializeToString method is detected.\ntype is {type(proto)}"
+        )
+
+    def deserialize_proto(self, serialized: bytes, proto: _Proto) -> _Proto:
+        if not isinstance(serialized, bytes):
+            raise TypeError(
+                f"Parameter 'serialized' must be bytes, but got type: {type(serialized)}"
+            )
+        decoded = typing.cast("Optional[int]", proto.ParseFromString(serialized))
+        if decoded is not None and decoded != len(serialized):
+            raise google.protobuf.message.DecodeError(
+                f"Protobuf decoding consumed too few bytes: {decoded} out of {len(serialized)}"
+            )
+        return proto
+
+
+class _TextProtoSerializer(ProtoSerializer):
+    """Serialize and deserialize text proto."""
+
+    supported_format = "textproto"
+    file_extensions = frozenset({".txtpb", ".textproto", ".prototxt", ".pbtxt"})
+
+    def serialize_proto(self, proto: _Proto) -> bytes:
+        textproto = google.protobuf.text_format.MessageToString(proto)
+        return textproto.encode(_ENCODING)
+
+    def deserialize_proto(self, serialized: bytes | str, proto: _Proto) -> _Proto:
+        if not isinstance(serialized, (bytes, str)):
+            raise TypeError(
+                f"Parameter 'serialized' must be bytes or str, but got type: {type(serialized)}"
+            )
+        if isinstance(serialized, bytes):
+            serialized = serialized.decode(_ENCODING)
+        assert isinstance(serialized, str)
+        return google.protobuf.text_format.Parse(serialized, proto)
+
+
+class _JsonSerializer(ProtoSerializer):
+    """Serialize and deserialize JSON."""
+
+    supported_format = "json"
+    file_extensions = frozenset({".json", ".onnxjson"})
+
+    def serialize_proto(self, proto: _Proto) -> bytes:
+        json_message = google.protobuf.json_format.MessageToJson(
+            proto, preserving_proto_field_name=True
+        )
+        return json_message.encode(_ENCODING)
+
+    def deserialize_proto(self, serialized: bytes | str, proto: _Proto) -> _Proto:
+        if not isinstance(serialized, (bytes, str)):
+            raise TypeError(
+                f"Parameter 'serialized' must be bytes or str, but got type: {type(serialized)}"
+            )
+        if isinstance(serialized, bytes):
+            serialized = serialized.decode(_ENCODING)
+        assert isinstance(serialized, str)
+        return google.protobuf.json_format.Parse(serialized, proto)
+
+
+class _TextualSerializer(ProtoSerializer):
+    """Serialize and deserialize the ONNX textual representation."""
+
+    supported_format = "onnxtxt"
+    file_extensions = frozenset({".onnxtxt", ".onnxtext"})
+
+    def serialize_proto(self, proto: _Proto) -> bytes:
+        text = onnx.printer.to_text(proto)  # type: ignore[arg-type]
+        return text.encode(_ENCODING)
+
+    def deserialize_proto(self, serialized: bytes | str, proto: _Proto) -> _Proto:
+        warnings.warn(
+            "The onnxtxt format is experimental. Please report any errors to the ONNX GitHub repository.",
+            stacklevel=2,
+        )
+        if not isinstance(serialized, (bytes, str)):
+            raise TypeError(
+                f"Parameter 'serialized' must be bytes or str, but got type: {type(serialized)}"
+            )
+        if isinstance(serialized, bytes):
+            text = serialized.decode(_ENCODING)
+        else:
+            text = serialized
+        if isinstance(proto, onnx.ModelProto):
+            return onnx.parser.parse_model(text)  # type: ignore[return-value]
+        if isinstance(proto, onnx.GraphProto):
+            return onnx.parser.parse_graph(text)  # type: ignore[return-value]
+        if isinstance(proto, onnx.FunctionProto):
+            return onnx.parser.parse_function(text)  # type: ignore[return-value]
+        if isinstance(proto, onnx.NodeProto):
+            return onnx.parser.parse_node(text)  # type: ignore[return-value]
+        raise ValueError(f"Unsupported proto type: {type(proto)}")
+
+
+# Register default serializers
+registry = _Registry()
+registry.register(_ProtobufSerializer())
+registry.register(_TextProtoSerializer())
+registry.register(_JsonSerializer())
+registry.register(_TextualSerializer())
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/shape_inference.py b/pythonProject/.venv/Lib/site-packages/onnx/shape_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a4a696eaf814e6adc4844a7d3583b390fbc9f1e
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/shape_inference.py
@@ -0,0 +1,177 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""onnx shape inference. Shape inference is not guaranteed to be
+complete.
+
+"""
+
+from __future__ import annotations
+
+import os
+from typing import TYPE_CHECKING
+
+import onnx
+import onnx.onnx_cpp2py_export.shape_inference as C  # noqa: N812
+from onnx.onnx_pb import AttributeProto, FunctionProto, ModelProto, TypeProto
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+GraphInferencer = C.GraphInferencer
+InferenceContext = C.InferenceContext
+
+
+def infer_shapes(
+    model: ModelProto | bytes,
+    check_type: bool = False,
+    strict_mode: bool = False,
+    data_prop: bool = False,
+) -> ModelProto:
+    """Apply shape inference to the provided ModelProto.
+
+    Inferred shapes are added to the value_info field of the graph.
+
+    If the inferred values conflict with values already provided in the
+    graph, that means that the provided values are invalid (or there is a
+    bug in shape inference), and the result is unspecified.
+
+    Arguments:
+        model: ModelProto.
+        check_type: Checks the type-equality for input and output.
+        strict_mode: Stricter shape inference, it will throw errors if any;
+            Otherwise, simply stop if any error.
+        data_prop: Enables data propagation for limited operators to perform shape computation.
+
+    Returns:
+        (ModelProto) model with inferred shape information
+    """
+    if isinstance(model, (ModelProto, bytes)):
+        model_str = model if isinstance(model, bytes) else model.SerializeToString()
+        inferred_model_str = C.infer_shapes(
+            model_str, check_type, strict_mode, data_prop
+        )
+        return onnx.load_from_string(inferred_model_str)
+    if isinstance(model, str):
+        raise TypeError(
+            "infer_shapes only accepts ModelProto or bytes,"
+            "you can use infer_shapes_path for the model path (String)."
+        )
+
+    raise TypeError(
+        f"infer_shapes only accepts ModelProto or bytes, incorrect type: {type(model)}"
+    )
+
+
+def infer_shapes_path(
+    model_path: str | os.PathLike,
+    output_path: str | os.PathLike = "",
+    check_type: bool = False,
+    strict_mode: bool = False,
+    data_prop: bool = False,
+) -> None:
+    """Take model path for shape_inference.
+
+    This function is the same as :func:`infer_shape` but supports >2GB models.
+    The function outputs the inferred model to the `output_path`. The original model path
+    is used if not specified.
+    """
+    if isinstance(model_path, ModelProto):
+        raise TypeError(
+            "infer_shapes_path only accepts model Path (String),"
+            "you can use infer_shapes for the ModelProto."
+        )
+    try:
+        model_path = os.fspath(model_path)
+    except TypeError as exp:
+        raise TypeError(
+            "infer_shapes_path only accepts model path as a string or PathLike, "
+            f"incorrect model path type: {type(model_path)}"
+        ) from exp
+    try:
+        output_path = os.fspath(output_path)
+    except TypeError as exp:
+        raise TypeError(
+            "infer_shapes_path only accepts output path as a string or PathLike, "
+            f"incorrect output path type: {type(output_path)}"
+        ) from exp
+
+    if output_path == "":
+        output_path = model_path
+    C.infer_shapes_path(model_path, output_path, check_type, strict_mode, data_prop)
+
+
+def infer_node_outputs(
+    schema: onnx.defs.OpSchema,
+    node: onnx.NodeProto,
+    input_types: dict[str, onnx.TypeProto],
+    input_data: dict[str, onnx.TensorProto] | None = None,
+    input_sparse_data: dict[str, onnx.SparseTensorProto] | None = None,
+    opset_imports: list[onnx.OperatorSetIdProto] | None = None,
+    ir_version: int = onnx.IR_VERSION,
+) -> dict[str, onnx.TypeProto]:
+    if not schema.has_type_and_shape_inference_function:
+        return {}
+    if input_data is None:
+        input_data = {}
+    if input_sparse_data is None:
+        input_sparse_data = {}
+    if opset_imports is None:
+        passed_opset_imports = {}
+    else:
+        passed_opset_imports = {opset.domain: opset.version for opset in opset_imports}
+
+    # catch KeyError if node's input does not exist in input_types
+    passed_input_types = {
+        key: input_types[key].SerializeToString() for key in node.input if key != ""
+    }
+    # input_types will also be used as outer_scope_value_types so do not filter by node's input here
+    for key, value in input_types.items():
+        if key not in passed_input_types:
+            passed_input_types[key] = value.SerializeToString()
+    passed_input_data = {
+        key: input_data[key].SerializeToString()
+        for key in node.input
+        if key in input_data
+    }
+    passed_sparse_input_data = {
+        key: input_sparse_data[key].SerializeToString()
+        for key in node.input
+        if key in input_sparse_data
+    }
+
+    outputs = schema._infer_node_outputs(
+        node.SerializeToString(),
+        passed_input_types,
+        passed_input_data,
+        passed_sparse_input_data,
+        passed_opset_imports,
+        ir_version,
+    )  # type: ignore[call-arg]
+    return {key: onnx.TypeProto.FromString(out) for key, out in outputs.items()}
+
+
+def infer_function_output_types(
+    function: FunctionProto,
+    input_types: Sequence[TypeProto],
+    attributes: Sequence[AttributeProto],
+) -> list[TypeProto]:
+    """Apply type-and-shape-inference to given function body, with given input types
+    and given input attribute values.
+    """
+    result = C.infer_function_output_types(
+        function.SerializeToString(),
+        [x.SerializeToString() for x in input_types],
+        [x.SerializeToString() for x in attributes],
+    )
+
+    def to_type_proto(x) -> TypeProto:
+        type_proto = onnx.TypeProto()
+        type_proto.ParseFromString(x)
+        return type_proto
+
+    return [to_type_proto(x) for x in result]
+
+
+InferenceError = C.InferenceError
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/string_utils.h b/pythonProject/.venv/Lib/site-packages/onnx/string_utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8b56ccd92cca0a006c1ec9500258a22dbc8d34f
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/string_utils.h
@@ -0,0 +1,61 @@
+// Copyright (c) ONNX Project Contributors
+//
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include 
+#include 
+
+namespace ONNX_NAMESPACE {
+
+#if defined(__ANDROID__)
+template 
+std::string to_string(T value) {
+  std::ostringstream os;
+  os << value;
+  return os.str();
+}
+
+inline int stoi(const std::string& str) {
+  std::stringstream ss;
+  int n = 0;
+  ss << str;
+  ss >> n;
+  return n;
+}
+
+#else
+using std::stoi;
+using std::to_string;
+#endif // defined(__ANDROID__)
+
+inline void MakeStringInternal(std::stringstream& /*ss*/) {}
+
+template 
+inline void MakeStringInternal(std::stringstream& ss, const T& t) {
+  ss << t;
+}
+
+template 
+inline void MakeStringInternal(std::stringstream& ss, const T& t, const Args&... args) {
+  MakeStringInternal(ss, t);
+  MakeStringInternal(ss, args...);
+}
+
+template 
+std::string MakeString(const Args&... args) {
+  std::stringstream ss;
+  MakeStringInternal(ss, args...);
+  return std::string(ss.str());
+}
+
+// Specializations for already-a-string types.
+template <>
+inline std::string MakeString(const std::string& str) {
+  return str;
+}
+inline std::string MakeString(const char* c_str) {
+  return std::string(c_str);
+}
+} // namespace ONNX_NAMESPACE
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/subbyte.py b/pythonProject/.venv/Lib/site-packages/onnx/subbyte.py
new file mode 100644
index 0000000000000000000000000000000000000000..c997331190075e996a32edc1846a4062586f72f1
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/subbyte.py
@@ -0,0 +1,168 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import typing
+
+import numpy as np
+import numpy.typing as npt
+import typing_extensions
+
+if typing.TYPE_CHECKING:
+    from collections.abc import Sequence
+
+INT4_MIN = -8
+INT4_MAX = 7
+UINT4_MIN = 0
+UINT4_MAX = 15
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float32_to_4bit_unpacked(x: np.ndarray | float, signed: bool) -> np.ndarray:
+    """Cast to 4bit via rounding and clipping (without packing).
+
+    Args:
+        x: element to be converted
+        signed: boolean, whether to convert to signed int4.
+
+    Returns:
+        An ndarray with a single int4 element (sign-extended to int8/uint8)
+    """
+    dtype = np.int8 if signed else np.uint8
+    clip_low = INT4_MIN if signed else UINT4_MIN
+    clip_high = INT4_MAX if signed else UINT4_MAX
+    if not isinstance(x, np.ndarray):
+        x = np.asarray(x)
+
+    clipped = np.clip(x, clip_low, clip_high)
+    return np.rint(clipped).astype(dtype)  # type: ignore[no-any-return]
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float32x2_to_4bitx2(
+    val_low: np.dtype, val_high: np.dtype, signed: bool
+) -> np.ndarray:
+    """Cast two elements to 4bit (via rounding and clipping) and pack
+    to a single byte
+    Args:
+        val_low: element to be packed in the 4 LSB
+        val_high: element to be packed in the 4 MSB
+        signed: boolean, whether to convert to signed int4.
+
+    Returns:
+        An ndarray with a single int8/uint8 element, containing both int4 elements
+    """
+    i8_high = float32_to_4bit_unpacked(val_high, signed)  # type: ignore[arg-type]
+    i8_low = float32_to_4bit_unpacked(val_low, signed)  # type: ignore[arg-type]
+    return i8_high << 4 | i8_low & 0x0F
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def unpack_4bitx2(
+    x: npt.NDArray[np.uint8], dims: int | Sequence[int]
+) -> npt.NDArray[np.uint8]:
+    """Unpack an array of packed uint8 elements (4bitx2) into individual elements
+    (still represented as uint8)
+
+    Args:
+        x: Input data
+        dims: The shape of the output array.
+
+    Returns:
+        A array containing unpacked 4-bit elements (as int8/uint8)
+    """
+    res = np.empty([x.size * 2], dtype=np.uint8)
+    x_low = x & np.uint8(0x0F)
+    x_high = x & np.uint8(0xF0)
+    x_high >>= np.uint8(4)
+    res[0::2] = x_low
+    res[1::2] = x_high
+    if (
+        res.size == np.prod(dims) + 1
+    ):  # handle single-element padding due to odd number of elements
+        res = res.ravel()[:-1]
+    res = res.reshape(dims)
+    return res
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def unpack_single_4bitx2(
+    x: np.ndarray | np.dtype | float, signed: bool
+) -> tuple[np.ndarray, np.ndarray]:
+    def unpack_signed(x):
+        return np.where((x >> 3) == 0, x, x | 0xF0)
+
+    """Unpack a single byte 4bitx2 to two 4 bit elements
+    Args:
+        x: Input data
+        signed: boolean, whether to interpret as signed int4.
+    Returns:
+        A tuple of ndarrays containing int4 elements (sign-extended to int8/uint8)
+    """
+    if not isinstance(x, np.ndarray):
+        x = np.asarray(x)
+    x_low = x & 0x0F
+    x_high = x >> 4
+    x_low = unpack_signed(x_low) if signed else x_low
+    x_high = unpack_signed(x_high) if signed else x_high
+    dtype = np.int8 if signed else np.uint8
+    return (x_low.astype(dtype), x_high.astype(dtype))
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float32_to_float4e2m1_unpacked(values: np.ndarray) -> np.ndarray:
+    """Cast float32 to float4e2m1 (without packing).
+
+    Args:
+        values: element or array to be converted
+
+    Returns:
+        An ndarray with unpacked float4e2m1 elements (as uint8)
+    """
+    sign = np.where(np.signbit(values), 0x8, 0x0).astype(np.uint8)
+    magnitude = np.abs(values)
+    res = np.zeros(values.shape, dtype=np.uint8)
+    res[(magnitude > 0.25) & (magnitude < 0.75)] = 0x1  # noqa: PLR2004
+    res[(magnitude >= 0.75) & (magnitude <= 1.25)] = 0x2  # noqa: PLR2004
+    res[(magnitude > 1.25) & (magnitude < 1.75)] = 0x3  # noqa: PLR2004
+    res[(magnitude >= 1.75) & (magnitude <= 2.5)] = 0x4  # noqa: PLR2004
+    res[(magnitude > 2.5) & (magnitude < 3.5)] = 0x5  # noqa: PLR2004
+    res[(magnitude >= 3.5) & (magnitude <= 5.0)] = 0x6  # noqa: PLR2004
+    res[magnitude > 5.0] = 0x7  # noqa: PLR2004
+    res |= sign
+    res[np.isnan(values)] = 0x7
+    return res
+
+
+@typing_extensions.deprecated(
+    "Deprecated since 1.18. Scheduled to remove in 1.20. Consider using libraries like ml_dtypes for dtype conversion",
+    category=DeprecationWarning,
+)
+def float32x2_to_float4e2m1x2(val_low: np.ndarray, val_high: np.ndarray) -> np.ndarray:
+    """Cast two elements to float4e2m1 and pack to a single byte
+    Args:
+        val_low: element to be packed in the 4 LSB
+        val_high: element to be packed in the 4 MSB
+
+    Returns:
+        An ndarray with uint8 elements, containing both float4e2m1 elements
+    """
+    i8_high = float32_to_float4e2m1_unpacked(val_high)
+    i8_low = float32_to_float4e2m1_unpacked(val_low)
+    return i8_high << 4 | i8_low & 0x0F
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/utils.py b/pythonProject/.venv/Lib/site-packages/onnx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d4eba55233a27c1f5569de4ea0e2df652a9cefd
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/utils.py
@@ -0,0 +1,321 @@
+# Copyright (c) ONNX Project Contributors
+#
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import os
+import tarfile
+from collections import deque
+from typing import TYPE_CHECKING
+
+import onnx.checker
+import onnx.helper
+import onnx.shape_inference
+
+if TYPE_CHECKING:
+    from onnx.onnx_pb import (
+        FunctionProto,
+        ModelProto,
+        NodeProto,
+        TensorProto,
+        ValueInfoProto,
+    )
+
+
+class Extractor:
+    def __init__(self, model: ModelProto) -> None:
+        self.model = model
+        self.graph = self.model.graph
+        self.initializers: dict[str, TensorProto] = self._build_name2obj_dict(
+            self.graph.initializer
+        )
+        self.value_infos: dict[str, ValueInfoProto] = self._build_name2obj_dict(
+            self.graph.value_info
+        )
+        # Add input and output values (not included in the value_info for intermediate values)
+        self.value_infos.update(self._build_name2obj_dict(self.graph.input))
+        self.value_infos.update(self._build_name2obj_dict(self.graph.output))
+        self.outmap: dict[str, int] = self._build_output_dict(self.graph)
+
+    @staticmethod
+    def _build_name2obj_dict(objs) -> dict:
+        return {obj.name: obj for obj in objs}
+
+    @staticmethod
+    def _build_output_dict(graph) -> dict[str, int]:
+        output_to_index: dict[str, int] = {}
+        for index, node in enumerate(graph.node):
+            for output_name in node.output:
+                if output_name == "":
+                    continue
+                assert output_name not in output_to_index  # output_name is unique
+                output_to_index[output_name] = index
+        return output_to_index
+
+    def _collect_new_io(self, io_names_to_extract: list[str]) -> list[ValueInfoProto]:
+        # Validate that all names exist in self.value_infos
+        missing_names = [
+            name for name in io_names_to_extract if name not in self.value_infos
+        ]
+        if missing_names:
+            raise ValueError(
+                f"The following names were not found in value_infos: {', '.join(missing_names)}"
+            )
+        return [self.value_infos[name] for name in io_names_to_extract]
+
+    def _dfs_search_reachable_nodes(
+        self,
+        node_output_name: str,
+        graph_input_names: set[str],
+        reachable: set[int],
+    ) -> None:
+        """Helper function to find nodes which are connected to an output
+
+        Arguments:
+            node_output_name (str): The name of the output
+            graph_input_names (set of string): The names of all inputs of the graph
+            reachable (set of int): The set of indexes to reachable nodes in `nodes`
+        """
+        stack = [node_output_name]
+        while stack:
+            current_output_name = stack.pop()
+            # finish search at inputs
+            if current_output_name in graph_input_names:
+                continue
+            # find nodes connected to this output
+            if current_output_name in self.outmap:
+                index = self.outmap[current_output_name]
+                if index not in reachable:
+                    # add nodes connected to this output to sets
+                    reachable.add(index)
+                    stack += [
+                        input_name
+                        for input_name in self.graph.node[index].input
+                        if input_name != ""
+                    ]
+
+    def _collect_reachable_nodes(
+        self,
+        input_names: list[str],
+        output_names: list[str],
+    ) -> list[NodeProto]:
+        _input_names = set(input_names)
+        reachable: set[int] = set()
+        for name in output_names:
+            self._dfs_search_reachable_nodes(name, _input_names, reachable)
+        # needs to be topologically sorted
+        return [self.graph.node[index] for index in sorted(reachable)]
+
+    def _collect_referred_local_functions(
+        self,
+        nodes: list[NodeProto],
+    ) -> list[FunctionProto]:
+        # a node in a model graph may refer a function.
+        # a function contains nodes, some of which may in turn refer a function.
+        # we need to find functions referred by graph nodes and
+        # by nodes used to define functions.
+        function_map: dict[tuple[str, str], FunctionProto] = {}
+        for function in self.model.functions:
+            function_map[(function.name, function.domain)] = function
+        referred_local_functions: list[FunctionProto] = []
+        queue = deque(nodes)
+        while queue:
+            node = queue.popleft()
+            # check if the node is a function op
+            if (node.op_type, node.domain) in function_map:
+                function = function_map.pop((node.op_type, node.domain))
+                referred_local_functions.append(function)
+                queue.extend(function.node)
+        # needs to be topologically sorted
+        return referred_local_functions
+
+    def _collect_reachable_tensors(
+        self,
+        nodes: list[NodeProto],
+    ) -> tuple[list[TensorProto], list[ValueInfoProto]]:
+        all_tensors_names: set[str] = set()
+        for node in nodes:
+            all_tensors_names.update(node.input)
+            all_tensors_names.update(node.output)
+        initializer = [
+            self.initializers[t] for t in self.initializers if t in all_tensors_names
+        ]
+        value_info = [
+            self.value_infos[t] for t in self.value_infos if t in all_tensors_names
+        ]
+        len_sparse_initializer = len(self.graph.sparse_initializer)
+        if len_sparse_initializer != 0:
+            raise ValueError(
+                f"len_sparse_initializer is {len_sparse_initializer}, it must be 0."
+            )
+        len_quantization_annotation = len(self.graph.quantization_annotation)
+        if len_quantization_annotation != 0:
+            raise ValueError(
+                f"len_quantization_annotation is {len_quantization_annotation}, it must be 0."
+            )
+        return initializer, value_info
+
+    def _make_model(
+        self,
+        nodes: list[NodeProto],
+        inputs: list[ValueInfoProto],
+        outputs: list[ValueInfoProto],
+        initializer: list[TensorProto],
+        value_info: list[ValueInfoProto],
+        local_functions: list[FunctionProto],
+    ) -> ModelProto:
+        name = "Extracted from {" + self.graph.name + "}"
+        graph = onnx.helper.make_graph(
+            nodes, name, inputs, outputs, initializer=initializer, value_info=value_info
+        )
+        meta = {
+            "ir_version": self.model.ir_version,
+            "opset_imports": self.model.opset_import,
+            "producer_name": "onnx.utils.extract_model",
+            "functions": local_functions,
+        }
+        return onnx.helper.make_model(graph, **meta)
+
+    def extract_model(
+        self,
+        input_names: list[str],
+        output_names: list[str],
+    ) -> ModelProto:
+        inputs = self._collect_new_io(input_names)
+        outputs = self._collect_new_io(output_names)
+        nodes = self._collect_reachable_nodes(input_names, output_names)
+        initializer, value_info = self._collect_reachable_tensors(nodes)
+        local_functions = self._collect_referred_local_functions(nodes)
+        model = self._make_model(
+            nodes, inputs, outputs, initializer, value_info, local_functions
+        )
+        return model
+
+
+def extract_model(
+    input_path: str | os.PathLike,
+    output_path: str | os.PathLike,
+    input_names: list[str],
+    output_names: list[str],
+    check_model: bool = True,
+    infer_shapes: bool = True,
+) -> None:
+    """Extracts sub-model from an ONNX model.
+
+    The sub-model is defined by the names of the input and output tensors *exactly*.
+
+    Note: For control-flow operators, e.g. If and Loop, the _boundary of sub-model_,
+    which is defined by the input and output tensors, should not _cut through_ the
+    subgraph that is connected to the _main graph_ as attributes of these operators.
+
+    Note: When the extracted model size is larger than 2GB, the extra data will be saved in "output_path.data".
+
+    Arguments:
+        input_path (str | os.PathLike): The path to original ONNX model.
+        output_path (str | os.PathLike): The path to save the extracted ONNX model.
+        input_names (list of string): The names of the input tensors that to be extracted.
+        output_names (list of string): The names of the output tensors that to be extracted.
+        check_model (bool): Whether to run model checker on the original model and the extracted model.
+        infer_shapes (bool): Whether to infer the shapes of the original model.
+    """
+    if not os.path.exists(input_path):
+        raise ValueError(f"Invalid input model path: {input_path}")
+    if not output_path:
+        raise ValueError("Output model path shall not be empty!")
+    if not input_names:
+        raise ValueError("Input tensor names shall not be empty!")
+    if not output_names:
+        raise ValueError("Output tensor names shall not be empty!")
+
+    if len(input_names) != len(set(input_names)):
+        raise ValueError("Duplicate names found in the input tensor names.")
+    if len(output_names) != len(set(output_names)):
+        raise ValueError("Duplicate names found in the output tensor names.")
+
+    if check_model:
+        onnx.checker.check_model(input_path)
+
+    if infer_shapes and os.path.getsize(input_path) > onnx.checker.MAXIMUM_PROTOBUF:
+        onnx.shape_inference.infer_shapes_path(input_path, output_path)
+        model = onnx.load(output_path)
+    elif infer_shapes:
+        model = onnx.load(input_path, load_external_data=False)
+        model = onnx.shape_inference.infer_shapes(model)
+        base_dir = os.path.dirname(input_path)
+        onnx.load_external_data_for_model(model, base_dir)
+    else:
+        model = onnx.load(input_path)
+
+    e = Extractor(model)
+    extracted = e.extract_model(input_names, output_names)
+
+    if extracted.ByteSize() > onnx.checker.MAXIMUM_PROTOBUF:
+        location = os.path.basename(output_path) + ".data"
+        onnx.save(extracted, output_path, save_as_external_data=True, location=location)
+    else:
+        onnx.save(extracted, output_path)
+
+    if check_model:
+        onnx.checker.check_model(output_path)
+
+
+def _tar_members_filter(
+    tar: tarfile.TarFile, base: str | os.PathLike
+) -> list[tarfile.TarInfo]:
+    """Check that the content of ``tar`` will be extracted safely
+
+    Args:
+        tar: The tarball file
+        base: The directory where the tarball will be extracted
+
+    Returns:
+        list of tarball members
+    """
+    result = []
+    for member in tar:
+        member_path = os.path.join(base, member.name)
+        abs_base = os.path.abspath(base)
+        abs_member = os.path.abspath(member_path)
+        if not abs_member.startswith(abs_base):
+            raise RuntimeError(
+                f"The tarball member {member_path} in downloading model contains "
+                f"directory traversal sequence which may contain harmful payload."
+            )
+        if member.issym() or member.islnk():
+            raise RuntimeError(
+                f"The tarball member {member_path} in downloading model contains "
+                f"symbolic links which may contain harmful payload."
+            )
+        result.append(member)
+    return result
+
+
+def _extract_model_safe(
+    model_tar_path: str | os.PathLike, local_model_with_data_dir_path: str | os.PathLike
+) -> None:
+    """Safely extracts a tar file to a specified directory.
+
+    This function ensures that the extraction process mitigates against
+    directory traversal vulnerabilities by validating or sanitizing paths
+    within the tar file. It also provides compatibility for different versions
+    of the tarfile module by checking for the availability of certain attributes
+    or methods before invoking them.
+
+    Args:
+        model_tar_path: The path to the tar file to be extracted.
+        local_model_with_data_dir_path: The directory path where the tar file
+      contents will be extracted to.
+    """
+    with tarfile.open(model_tar_path) as model_with_data_zipped:
+        # Mitigate tarball directory traversal risks
+        if hasattr(tarfile, "data_filter"):
+            model_with_data_zipped.extractall(
+                path=local_model_with_data_dir_path, filter="data"
+            )
+        else:
+            model_with_data_zipped.extractall(
+                path=local_model_with_data_dir_path,
+                members=_tar_members_filter(
+                    model_with_data_zipped, local_model_with_data_dir_path
+                ),
+            )
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/version.py b/pythonProject/.venv/Lib/site-packages/onnx/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea5d18012b75465dea41ddb96f16be0a67f21689
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/version.py
@@ -0,0 +1,5 @@
+# This file is generated by setup.py. DO NOT EDIT!
+
+
+version = "1.19.0"
+git_version = "57b9c6a4f6eebb09ae1b34cfb632078518f72832"
diff --git a/pythonProject/.venv/Lib/site-packages/onnx/version_converter.py b/pythonProject/.venv/Lib/site-packages/onnx/version_converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..40b4cab5955d41a03deb0b57ef99c9bc0f4380e1
--- /dev/null
+++ b/pythonProject/.venv/Lib/site-packages/onnx/version_converter.py
@@ -0,0 +1,43 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+"""onnx version converter
+
+This enables users to convert their models between different opsets within the
+default domain ("" or "ai.onnx").
+"""
+
+from __future__ import annotations
+
+import onnx
+import onnx.onnx_cpp2py_export.version_converter as C  # noqa: N812
+from onnx import ModelProto
+
+
+def convert_version(model: ModelProto, target_version: int) -> ModelProto:
+    """Convert opset version of the ModelProto.
+
+    Arguments:
+        model: Model.
+        target_version: Target opset version.
+
+    Returns:
+        Converted model.
+
+    Raises:
+        RuntimeError when some necessary conversion is not supported.
+    """
+    if not isinstance(model, ModelProto):
+        raise TypeError(
+            f"VersionConverter only accepts ModelProto as model, incorrect type: {type(model)}"
+        )
+    if not isinstance(target_version, int):
+        raise TypeError(
+            f"VersionConverter only accepts int as target_version, incorrect type: {type(target_version)}"
+        )
+    model_str = model.SerializeToString()
+    converted_model_str = C.convert_version(model_str, target_version)
+    return onnx.load_from_string(converted_model_str)
+
+
+ConvertError = C.ConvertError