code: def test_spspmm(dtype, device):
      if dtype in {torch.half, torch.bfloat16}:
          return  # Not yet implemented.
  
      indexA = torch.tensor(
          [[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]], device=device
      )
      valueA = tensor([1, 2, 3, 4, 5], dtype, device)
      indexB = torch.tensor([[0, 2], [1, 0]], device=device)
      valueB = tensor([2, 4], dtype, device)
  
      indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
      assert indexC.tolist() == [[0, 1, 2], [0, 1, 1]]
      assert valueC.tolist() == [8, 6, 8]
  
imports: from itertools import product
  import pytest
  import torch
  from torch_sparse import SparseTensor, spspmm
  from torch_sparse.testing import devices, grad_dtypes, tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_spspmm(dtype, device) without return types