stable, which guarantees that the order of equivalent elements
        is preserved.
Keyword Arguments:
      out (tuple, optional) -- the output tuple of
      (Tensor, LongTensor) that can be optionally given to be used
      as output buffers
Example:
  >>> x = torch.randn(3, 4)
  >>> sorted, indices = torch.sort(x)
  >>> sorted
  tensor([[-0.2162,  0.0608,  0.6719,  2.3332],
          [-0.5793,  0.0061,  0.6058,  0.9497],
          [-0.5071,  0.3343,  0.9553,  1.0960]])
  >>> indices
  tensor([[ 1,  0,  2,  3],
          [ 3,  1,  0,  2],
          [ 0,  3,  1,  2]])

  >>> sorted, indices = torch.sort(x, 0)
  >>> sorted
  tensor([[-0.5071, -0.2162,  0.6719, -0.5793],
          [ 0.0608,  0.0061,  0.9497,  0.3343],
          [ 0.6058,  0.9553,  1.0960,  2.3332]])
  >>> indices
  tensor([[ 2,  0,  0,  1],
          [ 0,  1,  1,  2],
          [ 1,  2,  2,  0]])
  >>> x = torch.tensor([0, 1] * 9)
