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_text(self, doc):
      truncated_contexts = [
          context
          for context in self.batch_truncate_text(
              doc["contexts"], self.CONTEXT_LIMIT
          )
      ]
      answer_context = "\n".join(
          [
              (f"[題名]:{choice}\n" + f"[問題]:{context}")
              for choice, context in zip(
                  doc["choices"], truncated_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 10 for calling the JAQKETV1 obj's underlying member method code with example usage: obj.doc_to_text(doc) and returns: unknown