code: def test_call_function_text_generation():
      """Tests if target sentence from model and model wrapped in a function (mimics model agnostic scenario)
      produces the same ids.
      """
  
      torch = pytest.importorskip("torch")
      transformers = pytest.importorskip("transformers")
  
      name = "hf-internal-testing/tiny-random-BartModel"
      tokenizer = transformers.AutoTokenizer.from_pretrained(name)
      model = transformers.AutoModelForSeq2SeqLM.from_pretrained(name)
  
      # Define function
      def f(x):
          inputs = tokenizer(
              x.tolist(), return_tensors="pt", padding=True
          )
          with torch.no_grad():
              out = model.generate(**inputs)
          sentence = [
              tokenizer.decode(g, skip_special_tokens=True) for g in out
          ]
          return sentence
  
      text_generation_for_pretrained_model = shap.models.TextGeneration(
          model, tokenizer=tokenizer, device="cpu"
      )
      text_generation_for_model_agnostic_scenario = (
          shap.models.TextGeneration(f, device="cpu")
      )
  
      s = "This is a test statement for verifying text generation ids"
  
      target_sentence_ids_for_pretrained_model = (
          text_generation_for_pretrained_model(s)
      )
      target_sentence_for_pretrained_model = [
          tokenizer.decode(g, skip_special_tokens=True)
          for g in target_sentence_ids_for_pretrained_model
      ]
      target_sentence_for_model_agnostic_scenario = (
          text_generation_for_model_agnostic_scenario(s)
      )
  
      assert (
          target_sentence_for_pretrained_model[0]
          == target_sentence_for_model_agnostic_scenario[0]
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_call_function_text_generation() and returns: sentence