Result for 28E792F8E8AF9ABD0EE0562DE6E77B26B756C32F

Query result

Key Value
FileName./usr/lib/python3.6/site-packages/opt_einsum/__pycache__/blas.cpython-36.pyc
FileSize4833
MD59BF0F75165CAFFE73FEA92F7CC44E1BA
SHA-128E792F8E8AF9ABD0EE0562DE6E77B26B756C32F
SHA-256B7A1718D5CB5D954E97FFDFC5DD397E79C752BFAFC4ACD33A06D4A948844A525
SSDEEP96:s1+wJQmqd5/VJZ/P8rWkrBGlq366NkMh51Jpu2/jqQDuViKYt2r4rOWrD2qYIrts:ZwJNqDJxEvGG3h1227D4hYcEPD2Otc53
TLSHT16AA195C1BDC87738D1A3D2F2E2CE0227CA11966F23164451B0ADD0655F769B1237EA8C
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
MD50804850D86D99B08C7E8BDAC32DE8348
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
PackageNamepython3-opt-einsum
PackageReleasebp153.1.14
PackageVersion3.1.0
SHA-1BABEB90028D30A4E0512059C08D11C0D75E8FE1F
SHA-256A1F6D7B3EE6E36148C01338384A9BCA7DCB04E209813E3FC515E9173734C1EB4
Key Value
MD5BA1F6565235687CFA53AB7FCC5B18C02
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
PackageNamepython3-opt-einsum
PackageReleaselp152.2.1
PackageVersion3.1.0
SHA-10B3AFAB25BD9C7EBE8CEB00A8FABE2D516523195
SHA-256402A809948F171ABC939D6790DAA8B8AFC9613A3E76DD3381F28DBBF57BE5008