code: def convert_to_dialogData(
      premise_raw,
      hypo_raw,
      answer_raw,
      dialog_format=False,
      binary_classes=False,
  ):
      """
      Convert from NLI context to dialog text.
  
      :param premise_raw: raw premise extracted from jsonl file.
      :param hypo_raw: raw hypothesis extracted from jsonl file.
      :param answer_raw: raw answer extracted from jsonl file.
      :param dialog_format: if set True, omit the special tokens 'Hypothesis' and 'Premise' in the text.
      :param binary_classes: if set True, bucketize (neutral, entailment) into one (no_contradiction)
      :return: a tuple (question, answer, clas)
          - ``question`` (str) is a query and possibly context
          - ``answers`` (iter) is an iterable of label(s) for that query
          - ``clas`` (iter) is an iterable of label candidates that the student can choose from
      """
      premise_raw = premise_raw.strip("\n").strip("\t")
      hypo_raw = hypo_raw.strip("\n").strip("\t")
      clas = MULTINLI_LABELS
  
      if binary_classes:
          answer_raw = BICLASS_DICT[answer_raw]
          clas = BICLASS_LABELS
      if not dialog_format:
          premise_raw = MULTINLI_PREMISE_PREFIX + premise_raw
          hypo_raw = MULTINLI_HYPO_PREFIX + hypo_raw
  
      question = premise_raw + "\n" + hypo_raw
      answers = [answer_raw]
  
      return question, answers, clas
  
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 3 for calling a global function with example usage: convert_to_dialogData(premise_raw, hypo_raw, answer_raw, dialog_format, binary_classes) and returns: question,  answers,  clas