code: def test_tsf_predictions(n_estimators, n_intervals):
      """Test TSF predictions."""
      random_state = 1234
      X_train, y_train = load_unit_test(split="train")
      X_test, y_test = load_unit_test(split="test")
  
      features = [np.mean, np.std, _slope]
      steps = [
          (
              "transform",
              RandomIntervalFeatureExtractor(
                  random_state=random_state, features=features
              ),
          ),
          (
              "clf",
              DecisionTreeClassifier(
                  random_state=random_state, max_depth=2
              ),
          ),
      ]
      estimator = Pipeline(steps)
  
      clf1 = ComposableTimeSeriesForestClassifier(
          estimator=estimator,
          random_state=random_state,
          n_estimators=n_estimators,
      )
      clf1.fit(X_train, y_train)
      a = clf1.predict_proba(X_test)
  
      # default, semi-modular implementation using
      # RandomIntervalFeatureExtractor internally
      clf2 = ComposableTimeSeriesForestClassifier(
          random_state=random_state, n_estimators=n_estimators
      )
      clf2.fit(X_train, y_train)
      b = clf2.predict_proba(X_test)
  
      np.testing.assert_array_equal(a, b)
  
file_path: sktime/sktime/classification/ensemble/tests/test_ensemble.py
imports: import numpy
  import pytest
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import FunctionTransformer
  from sklearn.tree import DecisionTreeClassifier
  from sktime.classification.ensemble import ComposableTimeSeriesForestClassifier
  from sktime.datasets import load_unit_test
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.compose import FeatureUnion
  from sktime.transformations.panel.segment import RandomIntervalSegmenter
  from sktime.transformations.panel.summarize import RandomIntervalFeatureExtractor
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.utils._testing.panel import make_classification_problem
  from sktime.utils.slope_and_trend import _slope
label_desc: Use image node_id 3 for calling a global function with example usage: test_tsf_predictions(n_estimators, n_intervals) without return types