Result for 0D7EAF6C7106F86CD86823F8B5F72A1469735C64

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/opt_einsum/backends/__pycache__/theano.cpython-39.pyc
FileSize2239
MD5F2E28274B8790C8E2EDB62456328B883
SHA-10D7EAF6C7106F86CD86823F8B5F72A1469735C64
SHA-25640A3132F7161A78C18EB566A43ACC0A6931457A31CA2E6F5BCB5F6520CC34ED2
SSDEEP48:pJrqlK2GuluDhH3IMghZUGfhg36MHzbefeqX8M2Hy0VD83ZxjIcLb:ulluDhXCZP236MTk8HTD8rIO
TLSHT10741306879478979F7F9F6F20507A260232281BA66CD400B670969E71D923C51CB1B9C
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