text: import pytest
  from pandas.tests.extension.numpy_.test_numpy_nested import np
  from sklearn import clone
  from sklearn.dummy import DummyClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.utils import check_X_y
  
  from sklego.common import flatten
  from sklego.meta import EstimatorTransformer
  from tests.conftest import transformer_checks, nonmeta_checks, general_checks
  
  
  @pytest.mark.parametrize("test_fn", flatten([
      transformer_checks,
      nonmeta_checks,
      general_checks,
  ]))
  def test_estimator_checks(test_fn):
      trf = EstimatorTransformer(LinearRegression())
      test_fn(EstimatorTransformer.__name__, trf)
  
  
  def test_values_uniform(random_xy_dataset_clf):
      X, y = random_xy_dataset_clf
      X, y = check_X_y(X, y)
      clf = DummyClassifier(strategy='most_frequent')
      transformer = EstimatorTransformer(clone(clf))
      transformed = transformer.fit(X, y).transform(X)
  
      assert transformed.shape == (y.shape[0],)
      assert np.all(transformed == clf.fit(X, y).predict(X))
  
  
  def test_set_params():
      clf = DummyClassifier(strategy='most_frequent')
      transformer = EstimatorTransformer(clf)
  
      transformer.set_params(estimator__strategy='stratified')
      assert clf.strategy == 'stratified'
  
  
  def test_get_params():
      clf = DummyClassifier(strategy='most_frequent')
      transformer = EstimatorTransformer(clf)
  
      assert transformer.get_params() == {
          'estimator__constant': None,
          'estimator__random_state': None,
          'estimator': clf,
          'estimator__strategy': 'most_frequent',
          'predict_func': 'predict'
      }
  