Result for 303B9559D5111D87A21376C6C332BC4250167A92

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/__pycache__/contract.cpython-38.opt-1.pyc
FileSize29273
MD5EF12427F7FF24CD726586A339A1A7FDA
SHA-1303B9559D5111D87A21376C6C332BC4250167A92
SHA-25619CFFADE33EEC9ECD4D8200740A205844B88D1B99441609DF04521FACB00176D
SSDEEP768:cBJq4SyT5QDsRCu3o2eG83Jg+i2/1d0u9T:4qBu5e2R3o2ef3Jg+i2/1V9T
TLSHT1B9D2E6616DC28B75F4E2F2B625DF9191D334D23BB7696043324CA06A1F916E05ABF3C8
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
MD5ECDE59A9431F0AE11A176614805378BA
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
PackageNamepython38-opt-einsum
PackageRelease2.3
PackageVersion3.3.0
SHA-1A1397F9FDDD66B755A1087EF4347B275B7870549
SHA-25638C0B6EA3F55A9ADDEFB81D92506CC513B903D0285B4A4F8FFF5057E10072A4C
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