code: def test_graph_trainer():
      dataset = build_dataset_from_name("mutag")
      utils.graph_random_splits(dataset, 0.8, 0.1)
  
      lp_trainer = GraphClassificationFullTrainer(
          model="gin", init=False
      )
  
      lp_trainer.num_features = dataset[0].x.size(1)
      lp_trainer.num_classes = max([d.y for d in dataset]).item() + 1
      lp_trainer.num_graph_features = 0
      lp_trainer.initialize()
  
      print(lp_trainer.encoder.encoder)
      print(lp_trainer.decoder.decoder)
  
      lp_trainer.train(dataset, True)
      result = lp_trainer.evaluate(dataset, "test", "acc")
      print("Acc:", result)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_graph_trainer() without return types