Result for 14CBDBF694A384893819C7F11DCE58F02255D8A2

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/__pycache__/sharing.cpython-38.opt-1.pyc
FileSize6598
MD577A2A186EF74F5A165BC73D35B5A1E78
SHA-114CBDBF694A384893819C7F11DCE58F02255D8A2
SHA-256761E19A75A9A3700500D695BB0D787C6FD55DB927BBA5185598B64ECB9244216
SSDEEP192:BYZzsuC2pYo2o3ejMWULCHm7QWyO4MHmFNMBvqMw5rXq+UEj:BYTPP2o3ULULC7WyaG4FqxrHUEj
TLSHT121D1838165C189B2FDB6FA7A6087037047158137BF6AA146B40CE4DE8F8F291997DFC4
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