code: def test_lp_trainer():
      dataset = build_dataset_from_name("cora")
      data = dataset[0]
      data = train_test_split_edges(data, 0.1, 0.1)
      dataset = [data]
  
      lp_trainer = LinkPredictionTrainer(model="gcn", init=False)
  
      lp_trainer.num_features = data.x.size(1)
      lp_trainer.initialize()
      print(lp_trainer.encoder.encoder)
      print(lp_trainer.decoder.decoder)
  
      lp_trainer.train(dataset, True)
      result = lp_trainer.evaluate(dataset, "test", "auc")
      print(result)
  
imports: from autogl.module.train import LinkPredictionTrainer
  from autogl.datasets import build_dataset_from_name
  from torch_geometric.utils import train_test_split_edges
label_desc: Use image node_id 1 for calling a global function with example usage: test_lp_trainer() without return types