svd
abstract fun StructureND<T>.svd(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
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Singular Value Decomposition.
Computes the singular value decomposition of either a matrix or batch of matrices input. The singular value decomposition is represented as a triple Triple(U, S, V), such that input = U dot diagonalEmbedding(S) dot VH, where VH is the conjugate transpose of V. If input is a batch of tensors, then U, S, and VH are also batched with the same batch dimensions as input. For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
Receiver
the input.
Return
triple Triple(U, S, V).