code: def test_dummy_pipeline(task_type: str) -> None:
      if task_type == "classification":
          estimator_class = MyDummyClassifier
          data_maker = make_classification
      elif task_type == "regression":
          estimator_class = MyDummyRegressor
          data_maker = make_regression
      else:
          pytest.fail(task_type)
          return
  
      X, y = data_maker(random_state=0)
      estimator = estimator_class(
          feat_type={i: "numerical" for i in range(X.shape[1])},
          config=1,
          random_state=0,
      )
      estimator.fit(X, y)
      check_is_fitted(estimator)
  
      assert np.shape(X)[0] == np.shape(estimator.predict(X))[0]
  
      # make sure we comply with scikit-learn estimator API
      clone(estimator)
  
imports: import numpy
  from sklearn.base import clone
  from sklearn.datasets import make_classification, make_regression
  from sklearn.utils.validation import check_is_fitted
  from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: test_dummy_pipeline(task_type) without return types