Result for 3380D75C2BB0CDA116086307D91B004C3D0CB0F6

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/opt_einsum/backends/__pycache__/object_arrays.cpython-38.opt-1.pyc
FileSize2794
MD59856DE1286E31C673CBD5E59F72CDF60
SHA-13380D75C2BB0CDA116086307D91B004C3D0CB0F6
SHA-2562B108E11D8F8F62333ED454B87ABA1B5301BC50389CC9C6D68E8F05E0FB76335
SSDEEP48:Yj5ZYl7CWSTNwtN+AtksVrTRpQcEQapR2pnru7TDiPPB68j6t9fKI:YteU+N+WksVrNqQESraDiPPP6/p
TLSHT1AA5185892CC1EE38F4FBF2B3404F420FE32562871A1AA542704DC5E79F66194A6BA34D
hashlookup:parent-total2
hashlookup:trust60

Network graph view

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