code: def test_jumping_knowledge():
      num_nodes, channels, num_layers = 100, 17, 5
      xs = list(
          [torch.randn(num_nodes, channels) for _ in range(num_layers)]
      )
  
      model = JumpingKnowledge("cat")
      assert str(model) == "JumpingKnowledge(cat)"
  
      out = model(xs)
      assert out.size() == (num_nodes, channels * num_layers)
  
      if is_full_test():
          jit = torch.jit.script(model)
          assert torch.allclose(jit(xs), out)
  
      model = JumpingKnowledge("max")
      assert str(model) == "JumpingKnowledge(max)"
  
      out = model(xs)
      assert out.size() == (num_nodes, channels)
  
      if is_full_test():
          jit = torch.jit.script(model)
          assert torch.allclose(jit(xs), out)
  
      model = JumpingKnowledge("lstm", channels, num_layers)
      assert str(model) == (
          f"JumpingKnowledge(lstm, channels="
          f"{channels}, layers={num_layers})"
      )
  
      out = model(xs)
      assert out.size() == (num_nodes, channels)
  
      if is_full_test():
          jit = torch.jit.script(model)
          assert torch.allclose(jit(xs), out)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_jumping_knowledge() without return types