Result for 11FA375B3E9FD2BD3B13B8D03A2F170A40C57367

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/tests/__pycache__/test_backends.cpython-38.pyc
FileSize15692
MD5BBF8DC903F0FBDD3CFE2D7E94A62C6E9
SHA-111FA375B3E9FD2BD3B13B8D03A2F170A40C57367
SHA-256E777B962D608585498C11C58988B0FD5449D56BDEDEFC84EB8B15DDB80608DB5
SSDEEP384:fukggr1JsWh1B/SiIM4t3g1J5qkXU1hFiq:fukggRiUB/SNMe3kXULFR
TLSHT1F862C69765874D63FDB9F6FA878D531B3B14E26F12D79207C409E49F0C8058A38B425E
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5D0D68673AAA7F0F8025CD2E26F524ED5
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.
PackageNamepython3-opt-einsum
PackageRelease3.2
PackageVersion3.1.0
SHA-161717F590F6B4F708C57FDA189CE6EF59DDFBE4D
SHA-2564BB8F400012C11C8AC6E3BAFDD3A680ED68A1AAF6D08CA4FF6FFB0FE12BCE512