Result for 247D30F4C554BA3D4EFB0ED6B980381B3C63C355

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/opt_einsum/__pycache__/sharing.cpython-39.opt-1.pyc
FileSize6615
MD5A7C021786255C40A3B72168E6F9181FE
SHA-1247D30F4C554BA3D4EFB0ED6B980381B3C63C355
SHA-2560AAD784240E506D001D97E7E69B14D75465FDE6591C3A5DCD46ECED07B1357E9
SSDEEP192:ixpFzNuCepYo2F+jMWEL9Hm7+I6O41HXFN/oBF0Mw5AXqVUDp:ixpiHP2F0LEL9JI6n3sL0xAMUDp
TLSHT1A3D1848169C289B2FDA2FA7A6083437487158137BF6A9106B40CE4DE5F8F2859D7DBC4
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