code: def test_attentive_fp():
      model = AttentiveFP(
          8, 16, 32, edge_dim=3, num_layers=2, num_timesteps=2
      )
      assert str(model) == (
          "AttentiveFP(in_channels=8, hidden_channels=16, "
          "out_channels=32, edge_dim=3, num_layers=2, "
          "num_timesteps=2)"
      )
  
      x = torch.randn(4, 8)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      edge_attr = torch.randn(edge_index.size(1), 3)
      batch = torch.tensor([0, 0, 0, 0])
  
      out = model(x, edge_index, edge_attr, batch)
      assert out.size() == (1, 32)
  
      if is_full_test():
          jit = torch.jit.script(model)
          assert torch.allclose(
              jit(x, edge_index, edge_attr, batch), out
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_attentive_fp() without return types