tensor types.
  * **requires_grad** (*bool**, **optional*) -- If autograd should
    record operations on the returned tensor. Default: "False".

  * **check_invariants** (*bool**, **optional*) -- If sparse
    tensor invariants are checked. Default: as returned by
    "torch.sparse.check_sparse_tensor_invariants.is_enabled()",
    initially False.

Example:
  >>> i = torch.tensor([[0, 1, 1],
  ...                   [2, 0, 2]])
  >>> v = torch.tensor([3, 4, 5], dtype=torch.float32)
  >>> torch.sparse_coo_tensor(i, v, [2, 4])
  tensor(indices=tensor([[0, 1, 1],
                         [2, 0, 2]]),
         values=tensor([3., 4., 5.]),
         size=(2, 4), nnz=3, layout=torch.sparse_coo)

  >>> torch.sparse_coo_tensor(i, v)  # Shape inference
  tensor(indices=tensor([[0, 1, 1],
                         [2, 0, 2]]),
         values=tensor([3., 4., 5.]),
         size=(2, 3), nnz=3, layout=torch.sparse_coo)
