code: def test_feature_importances_single_feature_interval_and_estimator():
      """Test feature importances for single feature interval and estimator.
  
      Check results of a simple case of single estimator, single feature and single
      interval from different but equivalent implementations
      """
      random_state = 1234
  
      # Compute using default method
      features = [np.mean]
      steps = [
          (
              "transform",
              RandomIntervalFeatureExtractor(
                  n_intervals=1,
                  features=features,
                  random_state=random_state,
              ),
          ),
          ("clf", DecisionTreeClassifier()),
      ]
      base_estimator = Pipeline(steps)
      clf1 = ComposableTimeSeriesForestClassifier(
          estimator=base_estimator,
          random_state=random_state,
          n_estimators=1,
      )
      clf1.fit(X_train, y_train)
  
      # Extract the interval and the estimator, and compute using pipelines
      intervals = clf1.estimators_[0].steps[0][1].intervals_
      steps = [
          ("segment", IntervalSegmenter(intervals)),
          (
              "transform",
              FeatureUnion(
                  [
                      (
                          "mean",
                          TabularToSeriesAdaptor(
                              FunctionTransformer(
                                  func=np.mean, validate=False
                              )
                          ),
                      )
                  ]
              ),
          ),
          ("clf", clone(clf1.estimators_[0].steps[-1][1])),
      ]
      clf2 = Pipeline(steps)
      clf2.fit(X_train, y_train)
  
      # Check for feature importances obtained from the estimators
      fi_expected = (
          clf1.estimators_[0].steps[-1][1].feature_importances_
      )
      fi_actual = clf2.steps[-1][1].feature_importances_
      np.testing.assert_array_equal(fi_actual, fi_expected)
  
file_path: sktime/sktime/series_as_features/base/estimators/tests/test_feature_importances_.py
imports: import numpy
  import pytest
  from sklearn.base import clone
  from sklearn.pipeline import FeatureUnion, Pipeline
  from sklearn.preprocessing import FunctionTransformer
  from sklearn.tree import DecisionTreeClassifier
  from sktime.classification.ensemble._ctsf import ComposableTimeSeriesForestClassifier
  from sktime.transformations.panel.segment import IntervalSegmenter
  from sktime.transformations.panel.summarize._extract import RandomIntervalFeatureExtractor
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.utils._testing.panel import make_classification_problem
label_desc: Use image node_id 1 for calling a global function with example usage: test_feature_importances_single_feature_interval_and_estimator() without return types