code: def load_data(
      directory_prefix, train_file, test_file, name, url=None
  ):
      if not os.path.exists(directory_prefix):
          os.mkdir(directory_prefix)
      directory = directory_prefix + name + "/"
      train_file_path = directory + train_file
      test_file_path = directory + test_file
      if (not os.path.exists(train_file_path)) or (
          not os.path.exists(test_file_path)
      ):
          # fetch files from s3:
          print(
              "%s data not found locally, so fetching from %s"
              % (name, url)
          )
          zip_name = download(url, directory_prefix)
          unzip(zip_name, directory_prefix)
          os.remove(zip_name)
  
      train_data = TabularDataset(train_file_path)
      test_data = TabularDataset(test_file_path)
      return train_data, test_data
  
imports: import copy
  import os
  import shutil
  import uuid
  from contextlib import contextmanager
  from typing import List, Tuple
  import numpy
  import pandas
  import pytest
  from autogluon.common.utils.path_converter import PathConverter
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION
  from autogluon.core.data.label_cleaner import LabelCleaner
  from autogluon.core.models import AbstractModel, BaggedEnsembleModel
  from autogluon.core.stacked_overfitting.utils import check_stacked_overfitting_from_leaderboard
  from autogluon.core.utils import download, generate_train_test_split, infer_problem_type, unzip
  from autogluon.features.generators import AbstractFeatureGenerator, AutoMLPipelineFeatureGenerator
  from autogluon.tabular import TabularDataset, TabularPredictor
label_desc: Use image node_id 2 for calling the DatasetLoaderHelper obj's underlying member method code with example usage: obj.load_data(directory_prefix, train_file, test_file, name, url) and returns: train_data,  test_data