code: import pytest
  from sklearn import linear_model as sk_linear_model
  from sklearn.utils import estimator_checks
  
  from river import base, cluster, compat, linear_model, preprocessing
  
  
  @pytest.mark.parametrize(
      "estimator",
      [
          pytest.param(estimator, id=str(estimator))
          for estimator in [
              linear_model.LinearRegression(),
              linear_model.LogisticRegression(),
              preprocessing.StandardScaler(),
              cluster.KMeans(seed=42),
          ]
      ],
  )
  @pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning")
  def test_river_to_sklearn_check_estimator(estimator: base.Estimator):
      skl_estimator = compat.convert_river_to_sklearn(estimator)
      estimator_checks.check_estimator(skl_estimator)
  
  
  @pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning")
  def test_sklearn_check_twoway():
      estimator = sk_linear_model.SGDRegressor()
      river_estimator = compat.convert_sklearn_to_river(estimator)
      skl_estimator = compat.convert_river_to_sklearn(river_estimator)
      estimator_checks.check_estimator(skl_estimator)
  
apis: sklearn.linear_model.SGDRegressor sklearn.utils.estimator_checks.check_estimator