code: def test_metric_recipe():
      import transformers
  
      from textattack import AttackArgs, Attacker
      from textattack.attack_recipes import DeepWordBugGao2018
      from textattack.datasets import HuggingFaceDataset
      from textattack.metrics.recipe import AdvancedAttackMetric
      from textattack.models.wrappers import HuggingFaceModelWrapper
  
      model = transformers.AutoModelForSequenceClassification.from_pretrained(
          "distilbert-base-uncased-finetuned-sst-2-english"
      )
      tokenizer = transformers.AutoTokenizer.from_pretrained(
          "distilbert-base-uncased-finetuned-sst-2-english"
      )
      model_wrapper = HuggingFaceModelWrapper(model, tokenizer)
      attack = DeepWordBugGao2018.build(model_wrapper)
      dataset = HuggingFaceDataset("glue", "sst2", split="train")
      attack_args = AttackArgs(
          num_examples=1,
          log_to_csv="log.csv",
          checkpoint_interval=5,
          checkpoint_dir="checkpoints",
          disable_stdout=True,
      )
      attacker = Attacker(attack, dataset, attack_args)
      results = attacker.attack_dataset()
  
      adv_score = AdvancedAttackMetric(
          ["meteor_score", "perplexity"]
      ).calculate(results)
      assert adv_score["avg_attack_meteor_score"] == 0.71
  
label_desc: Use image node_id 3 for calling a global function with example usage: test_metric_recipe() without return types