code: def spspmm(indexA, valueA, indexB, valueB, m, k, n, coalesced=False):
      """Matrix product of two sparse tensors. Both input sparse matrices need to
      be coalesced (use the :obj:`coalesced` attribute to force).
  
      Args:
          indexA (:class:`LongTensor`): The index tensor of first sparse matrix.
          valueA (:class:`Tensor`): The value tensor of first sparse matrix.
          indexB (:class:`LongTensor`): The index tensor of second sparse matrix.
          valueB (:class:`Tensor`): The value tensor of second sparse matrix.
          m (int): The first dimension of first sparse matrix.
          k (int): The second dimension of first sparse matrix and first
              dimension of second sparse matrix.
          n (int): The second dimension of second sparse matrix.
          coalesced (bool, optional): If set to :obj:`True`, will coalesce both
              input sparse matrices. (default: :obj:`False`)
  
      :rtype: (:class:`LongTensor`, :class:`Tensor`)
      """
  
      A = SparseTensor(
          row=indexA[0],
          col=indexA[1],
          value=valueA,
          sparse_sizes=(m, k),
          is_sorted=not coalesced,
      )
      B = SparseTensor(
          row=indexB[0],
          col=indexB[1],
          value=valueB,
          sparse_sizes=(k, n),
          is_sorted=not coalesced,
      )
  
      C = matmul(A, B)
      row, col, value = C.coo()
  
      return torch.stack([row, col], dim=0), value
  
label_desc: Use image node_id 1 for calling a global function with example usage: spspmm(indexA, valueA, indexB, valueB, m, k, n, coalesced) and returns: torch,  value