code: def load_dataset(name: str, directory_prefix: str = "./datasets/"):
      dataset_info = copy.deepcopy(
          DatasetLoaderHelper.dataset_info_dict[name]
      )
      train_file = dataset_info.pop("train_file", "train_data.csv")
      test_file = dataset_info.pop("test_file", "test_data.csv")
      name_inner = dataset_info.pop("name")
      url = dataset_info.pop("url", None)
      train_data, test_data = DatasetLoaderHelper.load_data(
          directory_prefix=directory_prefix,
          train_file=train_file,
          test_file=test_file,
          name=name_inner,
          url=url,
      )
  
      return train_data, test_data, dataset_info
  
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 1 for calling the DatasetLoaderHelper obj's underlying member method code with example usage: obj.load_dataset(name, directory_prefix) and returns: train_data,  test_data,  dataset_info