code: def test(program, exe, dataset, model, feed_dict):
      """Testing and validating."""
      final_model = produce_model(
          exe, program, dataset, model, feed_dict
      )
      valid_result = evaluate(
          final_model, dataset.edge_types, dataset.valid_data
      )
      test_result = evaluate(
          final_model, dataset.edge_types, dataset.test_data
      )
  
      logging.info(
          "valid_AUC %.4f | valid_PR %.4f | valid_F1 %.4f"
          % (
              valid_result["AUC"],
              valid_result["PR"],
              valid_result["F1"],
          )
      )
      logging.info(
          "test_AUC %.4f | test_PR %.4f | test_F1 %.4f"
          % (test_result["AUC"], test_result["PR"], test_result["F1"])
      )
  
      return test_result
  
imports: import os
  import argparse
  import time
  import numpy
  import logging
  import pickle
  import pgl
  from pgl.utils import paddle_helper
  import paddle
  import paddle.fluid
  import paddle.fluid.layers
  from utils import *
  import Dataset
  import model
  from sklearn.metrics import auc, f1_score, precision_recall_curve, roc_auc_score
label_desc: Use image node_id 8 for calling a global function with example usage: test(program, exe, dataset, model, feed_dict) and returns: test_result