"input" has repeated singular values, where one may multiply the
     columns of the spanning subspace in U and V by a rotation
     matrix and the resulting vectors will span the same subspace.
     Different platforms, like NumPy, or inputs on different device
     types, may produce different U and V tensors.
Parameters:
      * input (Tensor) -- the input tensor of size (, m, n)
        where *** is zero or more batch dimensions consisting of (m,
        n)* matrices.
  * **some** (*bool**, **optional*) -- controls whether to compute
    the reduced or full decomposition, and consequently, the shape
    of returned *U* and *V*. Default: *True*.

  * **compute_uv** (*bool**, **optional*) -- controls whether to
    compute *U* and *V*. Default: *True*.

Keyword Arguments:
      out (tuple, optional) -- the output tuple of tensors
Example:
  >>> a = torch.randn(5, 3)
  >>> a
  tensor([[ 0.2364, -0.7752,  0.6372],
