code: def test_prob_threshold_on_unit_test_data():
      """Test of ProbabilityThresholdEarlyClassifier on unit test data."""
      # load unit test data
      X_train, y_train = load_unit_test(split="train", return_X_y=True)
      X_test, y_test = load_unit_test(split="test", return_X_y=True)
      indices = np.random.RandomState(0).choice(
          len(y_train), 10, replace=False
      )
  
      # train probability threshold
      pt = ProbabilityThresholdEarlyClassifier(
          random_state=0,
          classification_points=[6, 16, 24],
          probability_threshold=1,
          estimator=TimeSeriesForestClassifier(
              n_estimators=10, random_state=0
          ),
      )
      pt.fit(X_train, y_train)
  
      final_probas = np.zeros((10, 2))
      final_decisions = np.zeros(10)
  
      X_test = from_nested_to_3d_numpy(X_test)
      states = None
      for i in pt.classification_points:
          X = X_test[indices, :, :i]
          probas = pt.predict_proba(X)
          decisions, states = pt.decide_prediction_safety(
              X, probas, states
          )
  
          for n in range(10):
              if decisions[n] and final_decisions[n] == 0:
                  final_probas[n] = probas[n]
                  final_decisions[n] = i
  
      testing.assert_array_equal(final_probas, pt_unit_test_probas)
  
imports: import numpy
  from numpy import testing
  from sktime.classification.early_classification import ProbabilityThresholdEarlyClassifier
  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
label_desc: Use image node_id 1 for calling a global function with example usage: test_prob_threshold_on_unit_test_data() without return types