"torch.sparse_csr" are supported. Default: "torch.sparse_coo"
Examples:
Set the main and first two lower diagonals of a matrix:
  >>> diags = torch.arange(9).reshape(3, 3)
  >>> diags
  tensor([[0, 1, 2],
          [3, 4, 5],
          [6, 7, 8]])
  >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3))
  >>> s
  tensor(indices=tensor([[0, 1, 2, 1, 2, 2],
                         [0, 1, 2, 0, 1, 0]]),
         values=tensor([0, 1, 2, 3, 4, 6]),
         size=(3, 3), nnz=6, layout=torch.sparse_coo)
  >>> s.to_dense()
  tensor([[0, 0, 0],
          [3, 1, 0],
          [6, 4, 2]])

Change the output layout:
  >>> diags = torch.arange(9).reshape(3, 3)
  >>> diags
  tensor([[0, 1, 2],[3, 4, 5], [6, 7, 8])
  >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3), layout=torch.sparse_csr)
  >>> s
  tensor(crow_indices=tensor([0, 1, 3, 6]),
