torch.svd
torch.svd(input, some=True, compute_uv=True, *, out=None)
Computes the singular value decomposition of either a matrix or
   batch of matrices "input". The singular value decomposition is
   represented as a namedtuple (U, S, V), such that "input" = U
   \text{diag}(S) V^{\text{H}}. where V^{\text{H}} is the transpose of
   V for real inputs, and the conjugate transpose of V for complex
   inputs. If "input" is a batch of matrices, then U, S, and V
   are also batched with the same batch dimensions as "input".
If "some" is True (default), the method returns the reduced
   singular value decomposition. In this case, if the last two
   dimensions of "input" are m and n, then the returned U and
   V matrices will contain only min(n, m) orthonormal columns.
If "compute_uv" is False, the returned U and V will be zero-
   filled matrices of shape (m, m) and (n, n) respectively, and