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 process_results(self, doc, results):
      gold = doc["gold"]
  
      response = np.argmax(results)
      acc = 1.0 if response == gold else 0.0
      completion_len = np.array([float(len(i)) for i in doc["choices"]])
      acc_norm = (
          1.0 if np.argmax(results / completion_len) == gold else 0.0
      )
  
      out = {
          "acc": acc,
          "acc_norm": acc_norm,
      }
      # only include details if we were wrong
      if acc == 0.0:
          # without the cast it won't serialize
          response = int(response)
          out["details"] = {
              "question": doc["goal"],
              "choices": doc["choices"],
              "gold": doc["gold"],
              "response": response,
          }
      return out
  
imports: import os
  import inspect
  import datasets
  from lm_eval.base import MultipleChoiceTask, rf
  import numpy
label_desc: Use image node_id 16 for calling the JAQKETV1 obj's underlying member method code with example usage: obj.process_results(doc, results) and returns: out