


torch.sparse_coo_tensor(i, v, [2, 4],
      ...                         dtype=torch.float64,
      ...                         device=torch.device('cuda:0'))
      tensor(indices=tensor([[0, 1, 1],
                             [2, 0, 2]]),
             values=tensor([3., 4., 5.]),
             device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64,
             layout=torch.sparse_coo)



  # Create an empty sparse tensor with the following invariants:
  #   1. sparse_dim + dense_dim = len(SparseTensor.shape)
  #   2. SparseTensor._indices().shape = (sparse_dim, nnz)
  #   3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])
  #
  # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and
  # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0))
  >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
  tensor(indices=tensor([], size=(1, 0)),
         values=tensor([], size=(0,)),
