Hermitian matrix. The Cholesky decomposition gives less
     information about the matrix but is much faster to compute than
     the eigenvalue decomposition.
 "torch.linalg.eig()" for a (slower) function that computes the
 eigenvalue decomposition of a not necessarily Hermitian square
 matrix.

 "torch.linalg.svd()" for a (slower) function that computes the
 more general SVD decomposition of matrices of any shape.

 "torch.linalg.qr()" for another (much faster) decomposition that
 works on general matrices.

Parameters:
      * A (Tensor) -- tensor of shape (, n, n)* where *** is
        zero or more batch dimensions consisting of symmetric or
        Hermitian matrices.
  * **UPLO** (*'L'**, **'U'**, **optional*) -- controls whether to
    use the upper or lower triangular part of "A" in the
    computations. Default: *'L'*.

Keyword Arguments:
      out (tuple, optional) -- output tuple of two tensors.