code: def test_message_passing_with_aggr_module(aggr_module):
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      row, col = edge_index
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = MyAggregatorConv(aggr=aggr_module)
      assert isinstance(conv.aggr_module, aggr.Aggregation)
      out = conv(x, edge_index)
      assert out.size(0) == 4 and out.size(1) in {8, 16}
      assert torch.allclose(conv(x, adj1.t()), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x, adj2.t()), out)
  
imports: import copy
  from typing import Tuple, Union
  import pytest
  import torch
  from torch import Tensor
  from torch.nn import Linear
  import torch_geometric.typing
  from torch_geometric.nn import MessagePassing, aggr
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size, SparseTensor
  from torch_geometric.utils import add_self_loops, scatter, spmm, to_torch_csc_tensor
label_desc: Use image node_id 17 for calling a global function with example usage: test_message_passing_with_aggr_module(aggr_module) without return types