code: def test_default_configuration_classify(self):
      for i in range(2):
          X_train, Y_train, X_test, Y_test = get_dataset(
              dataset="digits", make_sparse=False
          )
          configuration_space = (
              ExtraTreesPreprocessorClassification.get_hyperparameter_search_space()
          )
          default = configuration_space.get_default_configuration()
          preprocessor = ExtraTreesPreprocessorClassification(
              random_state=1,
              **{hp_name: default[hp_name] for hp_name in default},
          )
          preprocessor.fit(X_train, Y_train)
          X_train_trans = preprocessor.transform(X_train)
          X_test_trans = preprocessor.transform(X_test)
  
          # fit a classifier on top
          classifier = RidgeClassifier()
          predictor = classifier.fit(X_train_trans, Y_train)
          predictions = predictor.predict(X_test_trans)
          accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
          self.assertAlmostEqual(
              accuracy, 0.87310261080752882, places=2
          )
  
file_path: auto-sklearn/test/test_pipeline/components/feature_preprocessing/test_extra_trees_classification.py
imports: import sklearn.metrics
  from sklearn.linear_model import RidgeClassifier
  from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_classification import ExtraTreesPreprocessorClassification
  from autosklearn.pipeline.util import PreprocessingTestCase, _test_preprocessing, get_dataset
label_desc: Use image node_id 2 for calling the ExtreTreesClassificationComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_classify() without return types