Result for 14A94A575C6D7B1D1690B7777C1C00DEA74F7287

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/backends/__pycache__/tensorflow.cpython-38.pyc
FileSize4421
MD5470876C8705ACC55061FA2B3571B54C2
SHA-114A94A575C6D7B1D1690B7777C1C00DEA74F7287
SHA-256F1563D7C4111F608D89486C12B3E8775584AA4A0E0A5D2369C3828B215D72A84
SSDEEP96:3PsQNl4W+ZCSZcZX+WaHqH6vQeY7W26j8twbYf/xcI:ZNweOWavqVuI
TLSHT1719165FD2C466B3AF975F6F25B6FD3422331E227335A91414B08D1AF09897C53972498
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