code: def test_fit_roar(
      dataset: str,
      task: int,
      bounds: Tuple[float, float],
      dask_client_single_worker: Client,
      make_automl: Callable[..., AutoML],
      make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]],
  ) -> None:
      """
      Parameters
      ----------
      dataset : str
          The name of the dataset
  
      task : int
          The task type of the dataset
  
      bounds : Tuple[float, float]
          The bounds the final score should be in, (lowest, upper)
  
      Fixtures
      --------
      make_automl : Callable[..., AutoML]
          Factory for making an AutoML instance
  
      make_sklearn_dataset : Callable[..., Tuple[np.ndarray, ...]]
          Factory for getting a dataset
  
      Expects
      -------
      * Should fit without a problem using a different smac object
      """
  
      def get_roar_object_callback(
          scenario_dict: Dict,
          seed: Optional[Union[int, np.random.RandomState]],
          ta: Callable,
          ta_kwargs: Dict,
          dask_client: Client,
          n_jobs: int,
          **kwargs: Any,
      ) -> ROAR:
          """Random online adaptive racing.
  
          http://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf
          """
          scenario = Scenario(scenario_dict)
          return ROAR(
              run_id=seed,
              scenario=scenario,
              rng=seed,
              tae_runner=ta,
              tae_runner_kwargs=ta_kwargs,
              dask_client=dask_client,
              n_jobs=n_jobs,
          )
  
      X_train, Y_train, X_test, Y_test = make_sklearn_dataset(dataset)
      automl = make_automl(
          initial_configurations_via_metalearning=0,
          get_smac_object_callback=get_roar_object_callback,
          dask_client=dask_client_single_worker,
      )
  
      automl.fit(X_train, Y_train, task=task)
  
      score = automl.score(X_test, Y_test)
      assert score > 0.8
  
file_path: auto-sklearn/test/test_automl/test_fit.py
imports: from typing import Any, Callable, Dict, Optional, Tuple, Union
  import numpy
  from dask.distributed import Client
  from smac.facade.roar_facade import ROAR
  from smac.scenario.scenario import Scenario
  from autosklearn.automl import AutoML
  from autosklearn.constants import MULTICLASS_CLASSIFICATION
  from pytest_cases import parametrize
label_desc: Use image node_id 1 for calling a global function with example usage: test_fit_roar(dataset, task, bounds, dask_client_single_worker, make_automl, make_sklearn_dataset) and returns: ROAR