code: def setup_data(path, dialog_format=False, binary_classes=False):
      """
      Set up data in DialogData format from path.
  
      :param path: path to the data file that stores the MNLI dataset
      :param dialog_format: if set True, omit the special tokens 'Hypothesis' and 'Premise' in the text.
      :param binary_classes:  if set True, bucketize neutral and entailment in one (not_contradiction)
      :return:  a tuple in the parlai.core.teachers.DialogData format ``((x, y, r, c, i), new_episode?)`` where the ``x``
              is the query/question and ``y`` is the answer/label, ``clas`` represents the ``c`` the avaiable choices.
              ``new_episode`` is set True in any NLI teacher.
      """
      print("loading: " + path)
  
      with PathManager.open(path, "r") as data_file:
          for pair_line in data_file:
              pair = json.loads(pair_line)
              if pair[MULTINLI_ANSWER_KEY] == "-":
                  continue
  
              question, answers, clas = convert_to_dialogData(
                  premise_raw=pair[MULTINLI_PREMISE_KEY],
                  hypo_raw=pair[MULTINLI_HYPO_KEY],
                  answer_raw=pair[MULTINLI_ANSWER_KEY],
                  dialog_format=dialog_format,
                  binary_classes=binary_classes,
              )
  
              yield (question, answers, None, clas), True
  
file_path: ParlAI/parlai/tasks/multinli/agents.py
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import copy
  import json
  import os
  from parlai.utils.io import PathManager
  from parlai.core.teachers import DialogTeacher
  from .build import build
label_desc: Use image node_id 2 for calling a global function with example usage: setup_data(path, dialog_format, binary_classes) without return types