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)
docstr: Returns a fewshot context string that is made up of a prepended description
  (if provided), the `num_fewshot` number of examples, and an appended prompt example.
  
  :param doc: str
      The document as returned from training_docs, validation_docs, or test_docs.
  :param num_fewshot: int
      The number of fewshot examples to provide in the returned context string.
  :param provide_description: bool
      Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
  :param rnd: random.Random
      The pseudo-random number generator used to randomly sample examples.
      WARNING: This is currently a required arg although it's optionalized with a default `None`.
  :param description: str
      The task's description that will be prepended to the fewshot examples.
  :returns: str
      The fewshot context.
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code: def fewshot_context(
      self,
      doc,
      num_fewshot,
      provide_description=None,
      rnd=None,
      description=None,
  ):
      """Returns a fewshot context string that is made up of a prepended description
      (if provided), the `num_fewshot` number of examples, and an appended prompt example.
  
      :param doc: str
          The document as returned from training_docs, validation_docs, or test_docs.
      :param num_fewshot: int
          The number of fewshot examples to provide in the returned context string.
      :param provide_description: bool
          Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
      :param rnd: random.Random
          The pseudo-random number generator used to randomly sample examples.
          WARNING: This is currently a required arg although it's optionalized with a default `None`.
      :param description: str
          The task's description that will be prepended to the fewshot examples.
      :returns: str
          The fewshot context.
      """
      assert (
          rnd is not None
      ), "A `random.Random` generator argument must be provided to `rnd`"
      assert not provide_description, (
          "The `provide_description` arg will be removed in future versions. To prepend "
          "a custom description to the context, supply the corresponding string via the "
          "`description` arg."
      )
      if provide_description is not None:
          # nudge people to not specify it at all
          print(
              "WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
          )
  
      if hasattr(self, "FEWSHOT_SEP"):
          FEWSHOT_SEP = self.FEWSHOT_SEP
      elif hasattr(self, "SEP"):
          FEWSHOT_SEP = f"{self.SEP}{self.SEP}"
      else:
          FEWSHOT_SEP = "\n\n"
  
      if description:
          description += FEWSHOT_SEP
      elif hasattr(self, "DESCRIPTION"):
          description = self.DESCRIPTION
      else:
          description = ""
  
      if num_fewshot == 0:
          labeled_examples = ""
      else:
          # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
          if self.has_training_docs():
              fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd)
          else:
              if self._fewshot_docs is None:
                  self._fewshot_docs = list(
                      self.validation_docs()
                      if self.has_validation_docs()
                      else self.test_docs()
                  )
  
              fewshotex = rnd.sample(
                  self._fewshot_docs, num_fewshot + 1
              )
  
              # get rid of the doc that's the one we're evaluating, if it's in the fewshot
              fewshotex = [x for x in fewshotex if x != doc][
                  :num_fewshot
              ]
  
          labeled_examples = (
              FEWSHOT_SEP.join(
                  [
                      self.doc_to_answering_text(doc)
                      + self.doc_to_target(doc)
                      for doc in fewshotex
                  ]
              )
              + FEWSHOT_SEP
          )
  
      example = self.doc_to_text(doc)
      return description + labeled_examples + example
  
imports: import os
  import inspect
  import datasets
  from lm_eval.base import MultipleChoiceTask, rf
  import numpy
label_desc: Use image node_id 13 for calling the JAQKETV1 obj's underlying member method code with example usage: obj.fewshot_context(doc, num_fewshot, provide_description, rnd, description) and returns: unknown