tensor(crow_indices=tensor([0, 1, 3, 6]),
             col_indices=tensor([0, 0, 1, 0, 1, 2]),
             values=tensor([0, 3, 1, 6, 4, 2]), size=(3, 3), nnz=6,
             layout=torch.sparse_csr)
      >>> s.to_dense()
      tensor([[0, 0, 0],
              [3, 1, 0],
              [6, 4, 2]])
Set partial diagonals of a large output:
  >>> diags = torch.tensor([[1, 2], [3, 4]])
  >>> offsets = torch.tensor([0, -1])
  >>> torch.sparse.spdiags(diags, offsets, (5, 5)).to_dense()
  tensor([[1, 0, 0, 0, 0],
          [3, 2, 0, 0, 0],
          [0, 4, 0, 0, 0],
          [0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0]])

Note:
 When setting the values along a given diagonal the index into the
 diagonal and the index into the row of "diagonals" is taken as
 the column index in the output. This has the effect that when
 setting a diagonal with a positive offset *k* the first value
 along that diagonal will be the value in position *k* of the row
