class_docstr: prompt format is taken from [日本語に特化した60億パラメータ規模のGPTモデルの構築と評価](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/H9-4.pdf)
class_docstr_tok: ["prompt","format","is","taken","from","[","\u65e5\u672c\u8a9e\u306b\u7279\u5316\u3057\u305f60\u5104\u30d1\u30e9\u30e1\u30fc\u30bf\u898f\u6a21\u306eGPT\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u3068\u8a55\u4fa1","]","(","https",":","\/\/www.anlp.jp\/proceedings\/annual_meeting\/2023\/pdf_dir\/H9-4.pdf",")"]
docstr: [問題]:question
  [選択肢]:[choice0, choice1, ..., choice4]
  [答え]:
docstr_tok: ["[","\u554f\u984c","]",":","question","[","\u9078\u629e\u80a2","]",":","[","choice0",",","choice1",",","...",",","choice4","]","[","\u7b54\u3048","]",":"]
code: def doc_to_text(self, doc):
      """
      [問題]:question
      [選択肢]:[choice0, choice1, ..., choice4]
      [答え]:
      """
      return (
          f"[問題]:{doc['goal']}\n"
          f"[選択肢]:[{', '.join(doc['choices'])}]\n"
          "[答え]:"
      )
  
imports: import os
  import warnings
  import time
  from lm_eval.base import MultipleChoiceTask, rf
  import numpy
label_desc: Use image node_id 7 for calling the JCommonsenseQA obj's underlying member method code with example usage: obj.doc_to_text(doc) and returns: str, doc, str, str, str