code: def __call__(self, output_text, **kwargs):
      try:
          from auditnlg.safety.exam import safety_scores
      except ImportError as e:
          print(
              "Could not import optional dependency: auditnlg\nPlease install manually with:\n pip install auditnlg\nFollowed by:\npip install -r requirements.txt"
          )
          raise e
  
      data = [{"output": output_text}]
  
      result = safety_scores(data=data, method="sensitive_topics")
      scores = result[1]["all_scores"][0]
      is_safe = scores["pred_class"] == "none"
      report = ""
      if not is_safe:
          report += f"Predicted class: {scores['pred_class']}\n"
          report += (
              "|"
              + "|".join(
                  f"{n:^10}"
                  for n in [
                      list(k.keys())[0] for k in scores["class_scores"]
                  ]
              )
              + "|\n"
          )
          report += (
              "|"
              + "|".join(
                  f"{n:^10.5}"
                  for n in [
                      list(k.values())[0]
                      for k in scores["class_scores"]
                  ]
              )
              + "|\n"
          )
      return "Sensitive Topics", is_safe, report
  
imports: import os
  import torch
  import warnings
  from typing import List
  from string import Template
  from enum import Enum
label_desc: Use image node_id 2 for calling the AuditNLGSensitiveTopics obj's underlying member method code with example usage: obj.__call__(output_text) and returns: str,  is_safe,  report