code: def test(self):
      # restored best saved model
      self.model.load_checkpoint(self.stopper.save_model_path)
  
      t0 = time.time()
      scores = self.translate(self.test_dataloader)
      dur = time.time() - t0
      format_str = (
          "Test examples: {} | Time: {:.2f}s |  Test scores:".format(
              self.num_test, dur
          )
      )
      format_str += self.metric_to_str(scores)
      print(format_str)
      self.logger.write(format_str)
  
      return scores
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.cnn import CNNDataset
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation.rouge import ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from graph4nlp.pytorch.modules.utils.summarization_utils import wordid2str
label_desc: Use image node_id 9 for calling the ModelHandler obj's underlying member method code with example usage: obj.test() and returns: scores