class_docstr: The OpinionsQAScenario dataset is from the paper "Whose Opinions Do Language Models Reflect?"
  [Santurkar et al., 2023].
  
  OpinionsQA is a QA dataset containing 1484 multiple-choice questions. Since the questions are inherently
  subjective, there isn't a single ground truth response. Instead, the object of interest is how
  the distribution of model responses compares to those obtained from human survey participants.
  
  As discussed in Santurkar et al., we consider prompting an LM:
  1. Without any context (zero-shot) to evaluate the "default" opinions reflected
      by it.
  2. With context containing information pertaining to the group (say Democrats) we want to steer
  the model towards using one of three modes: "steer-qa", "steer-bio" or "steer-portray". See the
  example below for an illustration fo the difference between the three steering modes.
  
  
  We prompt models using the following format
  
      <optional context>
  
      Question: <question>
      A. <reference>
      B. <reference>
      C. <reference>
      D. <reference>
      Answer: <A/B/C/D>
  
  For instance:
  
      <optional context>
  
      Question: How much, if at all, do you think the ease with which people can legally obtain guns contributes
      to gun violence in the country today?
      A. A great deal
      B. A fair amount
      C. Not too much
      D. Not at all
      E. Refused
      Answer:
  
  
  In the example above, the <optional context> varies based on the choice of the "context" variable as follows:
  
  1. If context="default", <optional context> is not used and the model is prompted with the question directly.
  
  2. If context="steer-qa", the group information (to steer the model towards) is passed in a QA format, e.g.:
  
      Question: In politics today, do you consider yourself a
      A. Republican
      B. Democrat
      C. Independent
      D. Something else
      E. Refused
      Answer: B
  
  3. If context="steer-bio", the group information is passed to the model as a free-text response
  to a prompt asking for demographic information, e.g.:
  
  Below you will be asked to provide a short description of your political affiliation and
  then answer some questions.
  
  Description: In politics today, I consider myself a Democrat.
  
  4. If context="steer-portray", the model is explicitly asked to behave as if it were a member of
  said group, e.g.:
  
  Answer the following question as if in politics today, you considered yourself a Democrat.
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code: def download_data(self):
      self.output_path: str = os.path.join(self.output_path, "data")
      if not os.path.exists(self.output_path):
          os.makedirs(self.output_path)
  
      DOWNLOAD_FILENAMES = [
          self.FILE_NAME.format(wave=wave)
          for wave in self.PEW_SURVEY_WAVES
      ]
      DOWNLOAD_FILENAMES += [
          f"{steer}.csv"
          for steer in ["steer-qa", "steer-bio", "steer-portray"]
      ]
      DOWNLOAD_FILENAMES += [
          "Pew_American_Trends_Panel_disagreement_500.csv"
      ]
  
      for filename in DOWNLOAD_FILENAMES:
          data_path: str = os.path.join(self.output_path, filename)
  
          source_url: str = self.CODALAB_URI_TEMPLATE.format(
              bundle=self.CODALAB_BUNDLE, filename=filename
          )
          ensure_file_downloaded(
              source_url=source_url,
              target_path=data_path,
              downloader_executable="gdown",
          )
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/opinions_qa_scenario.py
imports: import os
  import pandas
  from typing import List, Dict
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, TEST_SPLIT, CORRECT_TAG, PassageQuestionInput, Input, Output
label_desc: Use image node_id 2 for calling the OpinionsQAScenario obj's underlying member method code with example usage: obj.download_data() without return types