code: def test_two_hop():
      transform = TwoHop()
      assert str(transform) == "TwoHop()"
  
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      edge_attr = torch.tensor([1, 2, 3, 1, 2, 3], dtype=torch.float)
      data = Data(
          edge_index=edge_index, edge_attr=edge_attr, num_nodes=4
      )
  
      data = transform(data)
      assert len(data) == 3
      assert data.edge_index.tolist() == [
          [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3],
          [1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2],
      ]
      assert data.edge_attr.tolist() == [
          1,
          2,
          3,
          1,
          0,
          0,
          2,
          0,
          0,
          3,
          0,
          0,
      ]
      assert data.num_nodes == 4
  
      data = Data(edge_index=edge_index, num_nodes=4)
      data = transform(data)
      assert len(data) == 2
      assert data.edge_index.tolist() == [
          [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3],
          [1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2],
      ]
      assert data.num_nodes == 4
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_two_hop() without return types