class_docstr: The IMDb dataset is from the paper:
  https://ai.stanford.edu/~amaas/data/sentiment/
  
  IMDb is a text classification dataset containing 25,000 training reviews and 25,000 test reviews.
  Each sample contains a sentence with its corresponding sentiment (0: Negative, 1: Positive)
  
  We prompt models using the following format
  
      <passage>
      Sentiment:
  
      Target completion:
          <sentiment> (<sentiment>:Positive or Negative)
  
  Using an example from the training dataset, we have
  
  ```
  Very good drama although it appeared to have a few blank areas leaving the viewers
  to fill in the action for themselves.
  I can imagine life being this way for someone who can neither read nor write.
  This film simply smacked of the real world: the wife who is suddenly the sole supporter,
  the live-in relatives and their quarrels, the troubled child who gets knocked up and then,
  typically, drops out of school, a jackass husband who takes the nest egg and buys beer with it.
  2 thumbs up.
  Sentiment:
  Target completion:
      Positive
  ```
  
  The IMDB dataset has a contrast set, whose examples happen to be in the original train split. We thus
  assign all examples with valid contrast sets to the validation split, in addition to those
  from the original test set.
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code: def get_instances(self) -> List[Instance]:
      target_path: str = os.path.join(self.output_path, "data")
  
      instances: List[Instance] = []
  
      url: str = f'{"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"}'
      ensure_file_downloaded(
          source_url=url, target_path=target_path, unpack=True
      )
      contrast_map = self.get_contrast_map(target_path, "test")
      contrast_map.update(self.get_contrast_map(target_path, "dev"))
  
      for split in [TRAIN_SPLIT, VALID_SPLIT]:
          instances.extend(
              self.get_split_instances(target_path, split, contrast_map)
          )
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/imdb_scenario.py
imports: import os
  from typing import List, Dict, Optional
  from helm.common.general import ensure_file_downloaded
  from .scenario import Scenario, Instance, Reference, CORRECT_TAG, TRAIN_SPLIT, VALID_SPLIT, Input, Output
  from .imdb_scenario_pinned_file_order import listdir_with_pinned_file_order
label_desc: Use image node_id 4 for calling the IMDBScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances