Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	ECDE59A9431F0AE11A176614805378BA
PackageArch	noarch
PackageDescription	Optimized 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.
PackageMaintainer	https://bugs.opensuse.org
PackageName	python38-opt-einsum
PackageRelease	2.3
PackageVersion	3.3.0
SHA-1	A1397F9FDDD66B755A1087EF4347B275B7870549
SHA-256	38C0B6EA3F55A9ADDEFB81D92506CC513B903D0285B4A4F8FFF5057E10072A4C

Key	Value
MD5	5B2F621F42C556B3D50FC9B7D0EDEB19
PackageArch	noarch
PackageDescription	Optimized 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.
PackageName	python38-opt-einsum
PackageRelease	2.1
PackageVersion	3.3.0
SHA-1	E79B623191DE489EABC9F4BCB1335B5F09EF5164
SHA-256	F8F32840CB9C48B1C0945FCA87D8F355D800EBE0BBD35A171831263CE912B27F

Key	Value
MD5	D0D68673AAA7F0F8025CD2E26F524ED5
PackageArch	noarch
PackageDescription	Optimized 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.
PackageName	python3-opt-einsum
PackageRelease	3.2
PackageVersion	3.1.0
SHA-1	61717F590F6B4F708C57FDA189CE6EF59DDFBE4D
SHA-256	4BB8F400012C11C8AC6E3BAFDD3A680ED68A1AAF6D08CA4FF6FFB0FE12BCE512