code: def case_regressor_fitted_holdout(
      make_cache: Callable[[str], Cache],
      dataset: str,
      make_backend: Callable[..., Backend],
      make_automl_regressor: Callable[..., AutoMLRegressor],
      make_sklearn_dataset: Callable[..., Tuple[np.ndarray, ...]],
  ) -> AutoMLRegressor:
      """Case of fitted regressor with holdout"""
      resampling_strategy = "holdout"
  
      key = f"case_regressor_{resampling_strategy}_{dataset}"
  
      # This locks the cache for this item while we check, required for pytest-xdist
      with make_cache(key) as cache:
          if "model" not in cache:
              model = make_automl_regressor(
                  temporary_directory=cache.path("backend"),
                  resampling_strategy=resampling_strategy,
                  delete_tmp_folder_after_terminate=False,
              )
  
              X, y, Xt, yt = make_sklearn_dataset(name=dataset)
              model.fit(X, y, dataset_name=dataset)
  
              cache.save(model, "model")
  
      # Try the model from the cache
      model = cache.load("model")
      model._backend = copy_backend(
          old=model._backend, new=make_backend()
      )
  
      return model
  
imports: from __future__ import annotations
  from typing import Callable, Tuple
  from pathlib import Path
  import numpy
  import sklearn.model_selection
  import autosklearn.metrics
  from autosklearn.automl import AutoMLClassifier, AutoMLRegressor
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor
  from pytest_cases import case, parametrize
  from test.fixtures.backend import copy_backend
  from test.fixtures.caching import Cache
label_desc: Use image node_id 7 for calling a global function with example usage: case_regressor_fitted_holdout(make_cache, dataset, make_backend, make_automl_regressor, make_sklearn_dataset) and returns: model