code: def test_tabularHPObagstack():
      ############ Benchmark options you can set: ########################
      perf_threshold = 1.1  # How much worse can performance on each dataset be vs previous performance without warning
      seed_val = 10000  # random seed
      subsample_size = None
      hyperparameter_tune_kwargs = {
          "scheduler": "local",
          "searcher": "auto",
      }
      num_stack_levels = 2
      num_bag_folds = 2
      verbosity = 2  # how much output to print
      hyperparameters = None
      time_limit = None
      fast_benchmark = True  # False
      # If True, run a faster benchmark (subsample training sets, less epochs, etc),
      # otherwise we run full benchmark with default AutoGluon settings.
      # performance_value warnings are disabled when fast_benchmark = True.
  
      #### If fast_benchmark = True, can control model training time here. Only used if fast_benchmark=True ####
      if fast_benchmark:
          subsample_size = 100
          nn_options = {
              "num_epochs": 2,
              "learning_rate": space.Real(0.001, 0.01),
          }
          gbm_options = {
              "num_boost_round": 20,
              "learning_rate": space.Real(0.01, 0.1),
          }
          hyperparameters = {"GBM": gbm_options, "NN_TORCH": nn_options}
          time_limit = 50
  
      fit_args = {
          "num_bag_folds": num_bag_folds,
          "num_stack_levels": num_stack_levels,
          "verbosity": verbosity,
      }
      if hyperparameter_tune_kwargs is not None:
          fit_args[
              "hyperparameter_tune_kwargs"
          ] = hyperparameter_tune_kwargs
      if hyperparameters is not None:
          fit_args["hyperparameters"] = hyperparameters
      if time_limit is not None:
          fit_args["time_limit"] = time_limit
          fit_args["num_bag_sets"] = 2
      ###################################################################
      run_tabular_benchmarks(
          fast_benchmark=fast_benchmark,
          subsample_size=subsample_size,
          perf_threshold=perf_threshold,
          seed_val=seed_val,
          fit_args=fit_args,
      )
  
imports: import os
  import shutil
  import sys
  import tempfile
  import warnings
  from random import seed
  import numpy
  import pandas
  import pytest
  from networkx.exception import NetworkXError
  from autogluon.common import space
  from autogluon.common.utils.simulation_utils import convert_simulation_artifacts_to_tabular_predictions_dict
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, QUANTILE, REGRESSION
  from autogluon.core.utils import download, unzip
  from autogluon.tabular import TabularDataset, TabularPredictor
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
label_desc: Use image node_id 12 for calling a global function with example usage: test_tabularHPObagstack() without return types