Result for 043A06521327FA86A67BE1C7E88F1D25467733E6

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/backends/__pycache__/jax.cpython-38.pyc
FileSize1355
MD595BD660E408112D81B967374FA4E8498
SHA-1043A06521327FA86A67BE1C7E88F1D25467733E6
SHA-2562582A3BA0BCF7F151CC8337E49825A29DD910A2F76FC00AD705EABBF51A20457
SSDEEP24:YpFtx7rUKAvE1iIEkJhhjGwnFqGO6B2Oz48KIlSN5pMZ6E8JwqqZ82gNi+Yx6wbm:anzAcgchyukZ6Bo8TKbrJwxIDBwhkaa
TLSHT1C2213FEC5A029FBDF2B4FAF22167E2111230917B33188183260C91AB4FC9286187368C
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