class_docstr: The Massive Multitask Language Understanding benchmark from this paper
  https://arxiv.org/pdf/2009.03300.pdf
  
  For InteractiveQA, we used a small subset of the original test set.
code: def get_instances(self) -> List[Instance]:
      # Download the MMLU data and the subsets for InteractiveQA
      data_path: str = os.path.join(self.output_path, "data")
      self.download_mmlu(data_path)
  
      test_path: str = os.path.join(
          data_path,
          "test",
          InteractiveQAMMLUScenario.INTERACTIVE_QA_DIR,
      )
      ensure_file_downloaded(
          source_url="https://worksheets.codalab.org/rest/bundles/0x4d49146fe16c4559bc64af6cbc04992d/"
          "contents/blob/",
          target_path=test_path,
          unpack=True,
          unpack_type="untar",
      )
  
      # Read all the instances
      instances: List[Instance] = []
      for mmlu_split in InteractiveQAMMLUScenario.MMLU_SPLITS:
          if mmlu_split == "test":
              split = TEST_SPLIT
              split_path = test_path
          else:
              split = TRAIN_SPLIT
              split_path = os.path.join(data_path, mmlu_split)
  
          csv_path: str = os.path.join(
              split_path, f"{self.subject}_{mmlu_split}.csv"
          )
          instances.extend(self.process_csv(csv_path, split))
  
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/interactive_qa_mmlu_scenario.py
imports: import os
  from typing import List
  from helm.common.general import ensure_file_downloaded
  from .scenario import Instance, TRAIN_SPLIT, TEST_SPLIT
  from .mmlu_scenario import MMLUScenario
label_desc: Use image node_id 2 for calling the InteractiveQAMMLUScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances