class_docstr: Transformers sentiment analysis pipeline returns a list of responses,
  like
      [{'label': 'POSITIVE', 'score': 0.7817379832267761}]
  We need to convert that to a format TextAttack understands, like
      [[0.218262017, 0.7817379832267761]
class_docstr_tok: ["Transformers","sentiment","analysis","pipeline","returns","a","list","of","responses",",","like","[","{","'label","'",":","'POSITIVE","'",",","'score","'",":","0.7817379832267761","}","]","We","need","to","convert","that","to","a","format","TextAttack","understands",",","like","[","[","0.218262017",",","0.7817379832267761","]"]
code: def __call__(self, text_inputs):
      raw_outputs = self.model(text_inputs)
      outputs = []
      for output in raw_outputs:
          score = output["score"]
          if output["label"] == "POSITIVE":
              outputs.append([1 - score, score])
          else:
              outputs.append([score, 1 - score])
      return np.array(outputs)
  
file_path: TextAttack/examples/attack/attack_camembert.py
imports: import os
  import numpy
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline
  from textattack import Attacker
  from textattack.attack_recipes import PWWSRen2019
  from textattack.datasets import HuggingFaceDataset
  from textattack.models.wrappers import ModelWrapper
label_desc: Use image node_id 2 for calling the HuggingFaceSentimentAnalysisPipelineWrapper obj's underlying member method code with example usage: obj.__call__(text_inputs) and returns: np