code: def __init__(self, opt: Opt):
      super().__init__(opt)
      if opt["noise_level"] > 1 or opt["noise_level"] < 0:
          raise Exception("Noise_level should be within 0 - 1.")
      if opt["noise_level"] == 0:
          logging.info(
              "No noise, teacher should be the same as before."
          )
      if opt["noise_level"] == 1:
          logging.info("Labels are exact the opposite.")
      self.labels = opt["classification_label_to_flip"].split(",")
      self.noise_level = opt["noise_level"]
      logging.info(f"Flipping with noise level {self.noise_level}")
      self.is_training = DatatypeHelper.is_training(opt["datatype"])
      self.flip_cnt = 0
      return
  
imports: from typing import Optional
  from parlai.core.message import Message
  from parlai.core.mutators import MessageMutator, register_mutator
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.utils.data import DatatypeHelper
  import hashlib
  import parlai.utils.logging
  import copy
label_desc: Use image node_id 2 to create a new FlipClassificationLabelMutator object from inherited base classes: MessageMutator with example: obj = FlipClassificationLabelMutator(opt)