code: def test_teaser_on_unit_test_data():
      """Test of TEASER on unit test data."""
      X_train, y_train, X_test, y_test, indices = load_unit_data()
  
      # train probability threshold
      teaser = TEASER(
          random_state=0,
          classification_points=[6, 10, 16, 24],
          estimator=TimeSeriesForestClassifier(
              n_estimators=10, random_state=0
          ),
      )
      teaser.fit(X_train, y_train)
  
      X_test = from_nested_to_3d_numpy(X_test)[indices]
      final_probas = np.zeros((10, 2))
      open_idx = np.arange(0, 10)
  
      for i in teaser.classification_points:
          probas, decisions = teaser.update_predict_proba(
              X_test[:, :, :i]
          )
          X_test, open_idx, final_idx = teaser.split_indices_and_filter(
              X_test, open_idx, decisions
          )
          final_probas[final_idx] = probas[decisions]
  
      testing.assert_array_equal(final_probas, teaser_unit_test_probas)
  
file_path: sktime/sktime/classification/early_classification/tests/test_teaser.py
imports: import numpy
  import pytest
  from numpy import testing
  from sklearn.ensemble import IsolationForest
  from sktime.classification.early_classification._teaser import TEASER
  from sktime.classification.interval_based import TimeSeriesForestClassifier
  from sktime.datasets import load_unit_test
  from sktime.datatypes._panel._convert import from_nested_to_3d_numpy
  from sktime.tests.test_switch import run_test_for_class
label_desc: Use image node_id 2 for calling a global function with example usage: test_teaser_on_unit_test_data() without return types