code: def test_teaser_near_classification_points():
      """Test of TEASER with incremental time stamps outside defined class points."""
      X_train, y_train, X_test, y_test, indices = load_unit_data()
  
      # train probability threshold
      teaser = TEASER(
          random_state=0,
          classification_points=[6, 10, 14, 18, 24],
          estimator=TimeSeriesForestClassifier(
              n_estimators=10, random_state=0
          ),
      )
      teaser.fit(X_train, y_train)
  
      # use test_points that are not within list above
      test_points = [7, 11, 19, 20]
  
      X_test = from_nested_to_3d_numpy(X_test)
      X_test = X_test[indices]
  
      decisions = np.zeros(len(X_test), dtype=bool)
      for i in test_points:
          X_test = X_test[np.invert(decisions)]
          X = X_test[:, :, :i]
  
          if i == 20:
              with pytest.raises(ValueError):
                  teaser.update_predict_proba(X)
          else:
              _, decisions = teaser.update_predict_proba(X)
  
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 4 for calling a global function with example usage: test_teaser_near_classification_points() without return types