code: def get_rvl_cdip_sample_data(
      zip_file="https://automl-mm-bench.s3.amazonaws.com/doc_classification/rvl_cdip_sample.zip",
      dataset_folder="rvl_cdip_sample",
      dataset_file_name="rvl_cdip_train_data.csv",
      download_dir="./ag_automm_doc_image",
  ):
      if os.path.exists(download_dir):
          shutil.rmtree(download_dir)
      load_zip.unzip(zip_file, unzip_dir=download_dir)
  
      dataset_path = os.path.join(download_dir, dataset_folder)
      rvl_cdip_data = pd.read_csv(f"{dataset_path}/{dataset_file_name}")
      train_data = rvl_cdip_data.sample(frac=0.8, random_state=200)
      test_data = rvl_cdip_data.drop(train_data.index)
  
      train_data[DOC_PATH_COL] = train_data[DOC_PATH_COL].apply(
          lambda ele: path_expander(ele, base_folder=download_dir)
      )
      test_data[DOC_PATH_COL] = test_data[DOC_PATH_COL].apply(
          lambda ele: path_expander(ele, base_folder=download_dir)
      )
      return train_data, test_data
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_classify_doc.py
imports: import os
  import shutil
  import tempfile
  from unittest import mock
  import numpy.testing
  import pandas
  import pytest
  import torch
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
  from ..utils.utils import get_home_dir
label_desc: Use image node_id 2 for calling a global function with example usage: get_rvl_cdip_sample_data(zip_file, dataset_folder, dataset_file_name, download_dir) and returns: train_data,  test_data