class_docstr: prompt template was inspired by [ChatGPT vs BERT: どちらが日本語をより理解できるのか?](https://fintan.jp/page/9126/)
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"] = [tup[1] for tup in choices_and_contexts]
      combined_context = "\n".join(
          [
              context
              for context in self.batch_truncate_text(
                  doc["contexts"], self.ANSWERING_CONTEXT_LIMIT
              )
          ]
      )
      answer_context = f"文章:{combined_context}"
      qa_prompt = self.doc_to_qa_prompt(doc)
      text = answer_context + "\n" + qa_prompt
      return text
  
imports: import os
  import inspect
  import datasets
  from lm_eval.base import MultipleChoiceTask, rf
  import numpy
label_desc: Use image node_id 3 for calling the JAQKETV1WithFintanPrompt obj's underlying member method code with example usage: obj.doc_to_answering_text(doc) and returns: text