class_docstr: The Massive Multitask Language Understanding benchmark from this paper:
  
  - https://arxiv.org/pdf/2009.03300.pdf
  
  Code is adapted from:
  
  - https://github.com/hendrycks/test/blob/master/evaluate.py
  - https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_test.py
  
  We prompt models using the following format
  
      <input>                  # train
      A. <reference>
      B. <reference>
      C. <reference>
      D. <reference>
      Answer: <A/B/C/D>
  
      x N (N-shot)
  
      <input>                  # test
      A. <reference1>
      B. <reference2>
      C. <reference3>
      D. <reference4>
      Answer:
  
  For example (from mmlu:anatomy), we have:
  
      The pleura
      A. have no sensory innervation.
      B. are separated by a 2 mm space.
      C. extend into the neck.
      D. are composed of respiratory epithelium.
      Answer: C
  
      Which of the following terms describes the body's ability to maintain its normal state?
      A. Anabolism
      B. Catabolism
      C. Tolerance
      D. Homeostasis
      Answer:
  
  Target: D
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code: def download_mmlu(self, path: str):
      ensure_file_downloaded(
          source_url="https://people.eecs.berkeley.edu/~hendrycks/data.tar",
          target_path=path,
          unpack=True,
          unpack_type="untar",
      )
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/mmlu_scenario.py
imports: import csv
  import os
  from typing import Dict, List
  from helm.common.general import ensure_file_downloaded
  from helm.common.hierarchical_logger import hlog
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, CORRECT_TAG, Input, Output
label_desc: Use image node_id 2 for calling the MMLUScenario obj's underlying member method code with example usage: obj.download_mmlu(path) without return types