code: def spspmm_sum(
      src: SparseTensor, other: SparseTensor
  ) -> SparseTensor:
      A = src.to_torch_sparse_coo_tensor()
      B = other.to_torch_sparse_coo_tensor()
      C = torch.sparse.mm(A, B)
      edge_index = C._indices()
      row, col = edge_index[0], edge_index[1]
      value: Optional[Tensor] = None
      if src.has_value() or other.has_value():
          value = C._values()
  
      return SparseTensor(
          row=row,
          col=col,
          value=value,
          sparse_sizes=(C.size(0), C.size(1)),
          is_sorted=True,
          trust_data=True,
      )
  
file_path: pytorch_sparse/torch_sparse/matmul.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch_sparse.tensor import SparseTensor
label_desc: Use image node_id 7 for calling a global function with example usage: spspmm_sum(src, other) and returns: SparseTensor