code: def test_my_edge_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      row, col = edge_index
      expected = scatter(
          x[row] - x[col], col, dim=0, dim_size=4, reduce="sum"
      )
  
      conv = MyEdgeConv()
      out = conv(x, edge_index)
      assert out.size() == (4, 16)
      assert torch.allclose(out, expected)
      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 9 for calling a global function with example usage: test_my_edge_conv() without return types