code: def test_my_edge_conv_jit():
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
  
      conv = MyEdgeConv()
      out = conv(x, edge_index)
  
      jit = torch.jit.script(conv)
      assert torch.allclose(jit(x, edge_index), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(jit(x, adj.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 10 for calling a global function with example usage: test_my_edge_conv_jit() without return types