old_contents: import random
  from interfaces.SentenceOperation import SentenceOperation
  from tasks.TaskTypes import TaskType
  
  # for sent tokenizer
  import spacy
  
  nlp = spacy.load("en_core_web_sm")
  
  # coref resolution from allennlp
  # ref: https://demo.allennlp.org/coreference-resolution
  import allennlp_models.tagging
  from allennlp.predictors.predictor import Predictor
  
  predictor = Predictor.from_path(
      "https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2021.03.10.tar.gz"
  )
  
  
  """
  Base Class for implementing the different input transformations a generation should be robust against.
  """
  
  
  def sentence_reordering(text, seed, coref_model):
      random.seed(seed)
      # resolve coref
      text = coref_model.coref_resolved(document=text)
  
      # tokenize and shuffle
      text_split = [i.text for i in nlp(text).sents]
      random.shuffle(text_split)
      return " ".join(text_split)
  
  
  """
  Shuffle sentence order
  """
  
  
  class SentenceReordering(SentenceOperation):
      tasks = [
          TaskType.TEXT_CLASSIFICATION,
          TaskType.TEXT_TO_TEXT_GENERATION,
      ]
      languages = ["en"]
  
      def __init__(self, seed=42, max_output=1):
          super().__init__(seed)
          self.max_output = max_output
          self.coref_model = Predictor.from_path(
              "https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2021.03.10.tar.gz"
          )
  
      def generate(self, sentence: str):
          pertubed = [
              sentence_reordering(
                  text=sentence, seed=self.seed, coref_model=self.coref_model
              )
          ]
          return pertubed
  
new_contents: import random
  from interfaces.SentenceOperation import SentenceOperation
  from tasks.TaskTypes import TaskType
  
  # for sent tokenizer
  import spacy
  
  # coref resolution from allennlp
  # ref: https://demo.allennlp.org/coreference-resolution
  import allennlp_models.tagging
  from allennlp.predictors.predictor import Predictor
  
  
  """
  Shuffle sentence order
  """
  
  
  class SentenceReordering(SentenceOperation):
      tasks = [
          TaskType.TEXT_CLASSIFICATION,
          TaskType.TEXT_TO_TEXT_GENERATION,
      ]
      languages = ["en"]
  
      def __init__(self, seed=42, max_output=1):
          super().__init__(seed)
          self.seed = seed
          self.nlp = spacy.load("en_core_web_sm")
          self.max_output = max_output
          self.coref_model = Predictor.from_path(
              "https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2021.03.10.tar.gz"
          )
  
      def generate(self, sentence: str):
          pertubed = [self.sentence_reordering(text=sentence)]
          return pertubed
  
      def sentence_reordering(self, text):
          random.seed(self.seed)
          # resolve coref
          text = self.coref_model.coref_resolved(document=text)
  
          # tokenize and shuffle
          text_split = [i.text for i in self.nlp(text).sents]
          random.shuffle(text_split)
          return " ".join(text_split)
  
subject: reformat the code