code: def test_traceable_my_conv_with_self_loops(num_nodes):
      # `torch.jit.trace` a `MessagePassing` layer that adds self loops and test
      # it across different input sizes.
      x = torch.randn(4, 16)
      edge_index = torch.tensor(
          [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]
      )
  
      conv = MyConvWithSelfLoops()
      traced_conv = torch.jit.trace(conv, ((x, edge_index)))
      scripted_conv = torch.jit.script(conv)
  
      x = torch.randn(num_nodes, 16)
      if num_nodes > 0:
          edge_index = torch.stack(
              [
                  torch.arange(0, num_nodes - 1),
                  torch.arange(1, num_nodes),
              ],
              dim=0,
          )
      else:
          edge_index = torch.empty((2, 0), dtype=torch.long)
  
      out = conv(x, edge_index)
      traced_out = traced_conv(x, edge_index)
      scripted_out = scripted_conv(x, edge_index)
  
      assert torch.allclose(out, traced_out)
      assert torch.allclose(out, scripted_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 19 for calling a global function with example usage: test_traceable_my_conv_with_self_loops(num_nodes) without return types