code: def fit_and_validate_dataset_with_cascade(
      dataset_name,
      fit_args,
      cascade: List[str],
      sample_size=1000,
      refit_full=True,
      delete_directory=True,
      expected_model_count=2,
  ):
      predictor = FitHelper.fit_and_validate_dataset(
          dataset_name=dataset_name,
          fit_args=fit_args,
          sample_size=sample_size,
          refit_full=refit_full,
          expected_model_count=expected_model_count,
          delete_directory=False,
      )
      directory_prefix = "./datasets/"
      (
          train_data,
          test_data,
          dataset_info,
      ) = DatasetLoaderHelper.load_dataset(
          name=dataset_name, directory_prefix=directory_prefix
      )
  
      predictor.predict(test_data, model=cascade)
      predictor.predict_proba(test_data, model=cascade)
  
      if delete_directory:
          shutil.rmtree(predictor.path, ignore_errors=True)
  
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 FitHelper obj's underlying member method code with example usage: obj.fit_and_validate_dataset_with_cascade(dataset_name, fit_args, cascade, sample_size, refit_full, delete_directory, expected_model_count) without return types