class_docstr: prompt format was inspired by [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)
code: def doc_to_answering_text(self, doc):
      choices_and_contexts = []
      for choice, context in zip(doc["choices"], doc["contexts"]):
          if doc["gold"] == choice:
              # need gold choice
              choices_and_contexts.append((choice, context))
          elif len(choices_and_contexts) < 2:
              # and wrong choice
              choices_and_contexts.append((choice, context))
          if 1 < len(choices_and_contexts):
              # 1 gold and 1 wrong are enough
              break
      doc["choices"] = [tup[0] for tup in choices_and_contexts]
      doc["contexts"] = self.batch_truncate_text(
          [tup[1] for tup in choices_and_contexts],
          self.ANSWERING_CONTEXT_LIMIT,
      )
      answer_context = "\n".join(
          [
              (f"[題名]:{choice}\n" + f"[問題]:{context}")
              for choice, context in zip(
                  doc["choices"], doc["contexts"]
              )
          ]
      )
      qa_prompt = self.doc_to_qa_prompt(doc)
      return answer_context + "\n" + qa_prompt
  
imports: import os
  import inspect
  import datasets
  from lm_eval.base import MultipleChoiceTask, rf
  import numpy
label_desc: Use image node_id 11 for calling the JAQKETV1 obj's underlying member method code with example usage: obj.doc_to_answering_text(doc) and returns: unknown