code: def create_single_dialog_train_example(context_dialog_path, candidate_dialog_paths, rng, positive_probability,
                                         minimum_context_length=2, max_context_length=20):
      
  
      dialog = translate_dialog_to_lists(context_dialog_path)
  
      context_str, next_utterance_ix = create_random_context(dialog, rng,
                                                             minimum_context_length=minimum_context_length,
                                                             max_context_length=max_context_length)
  
      if positive_probability > rng.random():
          # use the next utterance as positive example
          response = singe_user_utterances_to_string(dialog[next_utterance_ix])
          label = 1.0
      else:
          response = get_random_utterances_from_corpus(candidate_dialog_paths, rng, 1,
                                                       min_turn=minimum_context_length + 1,
                                                       max_turn=max_context_length)[0]
          label = 0.0
      return context_str, response, label
docs: Creates a single example for training set.
      :param context_dialog_path:
      :param candidate_dialog_paths:
      :param rng:
      :param positive_probability:
      :return: