code: def load_mnli_pandas_df(local_cache_path=".", file_split="train"):
      """Loads extracted test_utils into pandas
      Args:
          local_cache_path ([type], optional): [description].
              Defaults to current working directory.
          file_split (str, optional): The subset to load.
              One of: {"train", "dev_matched", "dev_mismatched"}
              Defaults to "train".
      Returns:
          pd.DataFrame: pandas DataFrame containing the specified
              MultiNLI subset.
      """
      try:
          download_file_and_extract(local_cache_path, file_split)
      except Exception as e:
          raise e
      return pd.read_json(
          os.path.join(local_cache_path, DATA_FILES[file_split]),
          lines=True,
      )
  
imports: import os
  import pandas
  from notebooks.test_utils.utils_data_shared import maybe_download, extract_zip
label_desc: Use image node_id 2 for calling a global function with example usage: load_mnli_pandas_df(local_cache_path, file_split) and returns: pd