Result for 254AE5190C9BE48B38C4E0AC4FA7BCCC4FA8B798

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/__pycache__/sharing.cpython-38.opt-1.pyc
FileSize6598
MD5EDF5435B3E63E4B2C485B2F04ED77A37
SHA-1254AE5190C9BE48B38C4E0AC4FA7BCCC4FA8B798
SHA-2568DDBC9EBF31A286BEED57C5365C4208AC267A66285C4A647C8F3E47E91A33660
SSDEEP192:PCYZzsuC2pYo2o3ejMWULCHm7tWdO4eHSFNMBfPMw5SXqVUVI:PCYTPP2o3ULULCGWdoy4ZPxSMUVI
TLSHT1F5D1838065C189B2FDB6FA7A6187037047158137BF6AA106B40CE4DE8F8F2919A7DFC4
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5ECDE59A9431F0AE11A176614805378BA
PackageArchnoarch
PackageDescriptionOptimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., `np.einsum`,`dask.array.einsum`,`pytorch.einsum`,`tensorflow.einsum`) by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API. See the [**documentation**](http://optimized-einsum.readthedocs.io) for more information.
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython38-opt-einsum
PackageRelease2.3
PackageVersion3.3.0
SHA-1A1397F9FDDD66B755A1087EF4347B275B7870549
SHA-25638C0B6EA3F55A9ADDEFB81D92506CC513B903D0285B4A4F8FFF5057E10072A4C
Key Value
MD55B2F621F42C556B3D50FC9B7D0EDEB19
PackageArchnoarch
PackageDescriptionOptimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., `np.einsum`,`dask.array.einsum`,`pytorch.einsum`,`tensorflow.einsum`) by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API. See the [**documentation**](http://optimized-einsum.readthedocs.io) for more information.
PackageNamepython38-opt-einsum
PackageRelease2.1
PackageVersion3.3.0
SHA-1E79B623191DE489EABC9F4BCB1335B5F09EF5164
SHA-256F8F32840CB9C48B1C0945FCA87D8F355D800EBE0BBD35A171831263CE912B27F