code: def test_predict_proba2(
      model, pred_types, use_calibration, binarize_preds
  ):
      """
      Checks if the `aux_predict_proba` is set properly to the model's `decision_function` when ``preds_type=scores``
      and ``binarize_preds=False``.
      """
      drift_detector = ClassifierDriftSklearn(
          x_ref=np.random.randn(100, 5),
          model=model,
          preds_type=pred_types,
          use_calibration=use_calibration,
          binarize_preds=binarize_preds,
      )
  
      # define train and test set for internal model
      x_tr, y_tr = np.random.randn(100, 5), np.random.randint(0, 2, 100)
      x_te = np.random.randn(100, 5)
  
      # extract and fit internal model
      internal_model = drift_detector.model
      internal_model.fit(x_tr, y_tr)
  
      # check if predict matches the new predict_proba
      np.testing.assert_allclose(
          internal_model.decision_function(x_te),
          internal_model.aux_predict_proba(x_te)[:, 1],
      )
  
imports: import pytest
  import numpy
  from typing import Union
  from alibi_detect.cd.sklearn.classifier import ClassifierDriftSklearn
  from sklearn.svm import SVC
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
label_desc: Use image node_id 10 for calling a global function with example usage: test_predict_proba2(model, pred_types, use_calibration, binarize_preds) without return types