code: def test_relevant_augmentor_cross_validated():
      """
      Validates that the RelevantFeatureAugmenter can be cloned in pipelines, see issue 537
      """
      n = 16  # number of samples, needs to be divisable by 4
      index = range(n)
      df_ts = pd.DataFrame(
          {
              "time": [10, 11] * n,
              "id": np.repeat(index, 2),
              "value": [0, 1] * (n // 4)
              + [1, 2] * (n // 4)
              + [10, 11] * (n // 4)  # class 0
              + [12, 14] * (n // 4),
          }
      )
      y = pd.Series(data=[0] * (n // 2) + [1] * (n // 2), index=index)
      X = pd.DataFrame(index=index)
      augmenter = RelevantFeatureAugmenter(
          column_id="id",
          column_sort="time",
          timeseries_container=df_ts,
          default_fc_parameters=MinimalFCParameters(),
          disable_progressbar=True,
          show_warnings=False,
          fdr_level=0.90,
      )
      pipeline = Pipeline(
          [
              ("augmenter", augmenter),
              ("classifier", RandomForestClassifier(random_state=1)),
          ]
      )
  
      scores = model_selection.cross_val_score(pipeline, X, y, cv=2)
      assert (scores == np.array([1, 1])).all()
  
imports: import mock
  import numpy
  import pandas
  from sklearn import model_selection
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from tests.fixtures import DataTestCase, warning_free
  from tsfresh.feature_extraction import MinimalFCParameters
  from tsfresh.transformers.relevant_feature_augmenter import RelevantFeatureAugmenter
label_desc: Use image node_id 1 for calling a global function with example usage: test_relevant_augmentor_cross_validated() without return types