Result for 026A596FC1B6EF47D76F65D3BAA70043366E1435

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/opt_einsum/tests/__pycache__/test_contract.cpython-39.opt-1.pyc
FileSize7270
MD58DDD9274E49714F7FED53FEE91A40E9A
SHA-1026A596FC1B6EF47D76F65D3BAA70043366E1435
SHA-256E91BE26C0A4DCB48C11A838CC2E6454A031C243CB644B629469845B963787534
SSDEEP96:VYYOgoaxSbP0WJDuRCFXhXquQjaJwZLl+IeZhuJWNKVIRjVbbR5rmt7vFDvQ83:SUoaS1DGih6uSbZAhiWN+AVbbR67W83
TLSHT15DE10A7058AF8E47FEE1F4F45229821C5334C3FE5BEEA012B52CE19E79C96E01675648
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
MD5F119898EA46EB4FDC77A85726360152E
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.
PackageNamepython39-opt-einsum
PackageRelease2.1
PackageVersion3.3.0
SHA-15277513FEDCEED387FBE66D283B02E23FABCB577
SHA-256F5209F8257294FF884575429B524C9185DE82AA496FDEB60695D3ACE28863AC3
Key Value
MD587600B2D04C9EED4EE94DAEA782A6F77
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
PackageNamepython39-opt-einsum
PackageRelease2.3
PackageVersion3.3.0
SHA-1020EE7C83F7F8158E12F0FB364BA780C2EB92933
SHA-25699DB37F55811B12AB5A277CC28CE9FF4AA828F12981791F3FA4987B4697E04F9