Result for 2083B947C9A8FC66B1EE39961F584893ACD7C869

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/__pycache__/parser.cpython-38.pyc
FileSize10090
MD54E315CA5FDAE95A24B0BFD2FD4C7A1C8
SHA-12083B947C9A8FC66B1EE39961F584893ACD7C869
SHA-25672FA9BF20053017D7BE1A77E869A6CDFB04AB6AB950EEF7D4D012CD64CAE4C8B
SSDEEP192:rZ6fYWkd1oF3AXEMUuw7Qdd2u0yvC6BoN7vQ3dcPvnwciiKWZ8NMk0A:BfUMZw7Qj06CbN7v+dc3ZjKG8NMk0A
TLSHT1BE22C7611CC21C28F6F7F2F604DF9697D32080B75AD4454BB089F0EA9F861A95939BAC
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
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
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