Result for 242CA68BAC50BFF8951DD38A2F1F22FEA1D5750E

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/opt_einsum/tests/__pycache__/test_input.cpython-39.pyc
FileSize7281
MD505710EAADC3433B18E5A8566E44C14B3
SHA-1242CA68BAC50BFF8951DD38A2F1F22FEA1D5750E
SHA-256977C3DFE320E08F901819BB2AE2452EC6416E480FC26EF39C45353DFF09DC76E
SSDEEP96:Swlt77ThwXurFnmYZdERy3ZZuqZacU0I3s2eZcrNRoS+9SRBuEuF+BaNu:d/GEr1N68nUNuEa+V
TLSHT1ADE122DB74161571FE80F37680EB1732269F93351708AE63ED18BA1B9D423D82A69EC4
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
MD5F119898EA46EB4FDC77A85726360152E
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.
PackageNamepython39-opt-einsum
PackageRelease2.1
PackageVersion3.3.0
SHA-15277513FEDCEED387FBE66D283B02E23FABCB577
SHA-256F5209F8257294FF884575429B524C9185DE82AA496FDEB60695D3ACE28863AC3
Key Value
MD587600B2D04C9EED4EE94DAEA782A6F77
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
PackageNamepython39-opt-einsum
PackageRelease2.3
PackageVersion3.3.0
SHA-1020EE7C83F7F8158E12F0FB364BA780C2EB92933
SHA-25699DB37F55811B12AB5A277CC28CE9FF4AA828F12981791F3FA4987B4697E04F9