torch.svd_lowrank
torch.svd_lowrank(A, q=6, niter=2, M=None)
Return the singular value decomposition "(U, S, V)" of a matrix,
   batches of matrices, or a sparse matrix A such that A \approx U
   diag(S) V^T. In case M is given, then SVD is computed for the
   matrix A - M.
Note:
 The implementation is based on the Algorithm 5.1 from Halko et
 al, 2009.

Note:
 To obtain repeatable results, reset the seed for the pseudorandom
 number generator

Note:
 The input is assumed to be a low-rank matrix.

Note:
 In general, use the full-rank SVD implementation
 "torch.linalg.svd()" for dense matrices due to its 10-fold higher
 performance characteristics. The low-rank SVD will be useful for
 huge sparse matrices that "torch.linalg.svd()" cannot handle.

Args::
      A (Tensor): the input tensor of size (*, m, n)
  q (int, optional): a slightly overestimated rank of A.

  niter (int, optional): the number of subspace iterations to
