code: def _get_ensemble_probabilities(
      classifier_ensemble, X, estimator_features=None
  ):
      """Get the probabilities estimate for each base classifier in the ensemble
  
      Parameters
      ----------
      classifier_ensemble : list of shape = [n_classifiers]
          Containing the ensemble of classifiers used in the aggregation scheme.
  
      X : array of shape (n_samples, n_features)
          The input data.
  
      estimator_features : array of shape (n_classifiers, n_selected_features)
          Indices containing the features used by each classifier.
  
      Returns
      -------
      list_proba : array of shape (n_samples, n_classifiers, n_classes)
          Probabilities predicted by each base classifier in the ensemble for all
          samples in X.
      """
      list_proba = []
      if estimator_features is None:
          for idx, clf in enumerate(classifier_ensemble):
              list_proba.append(clf.predict_proba(X))
      else:
          for idx, clf in enumerate(classifier_ensemble):
              list_proba.append(
                  clf.predict_proba(X[:, estimator_features[idx]])
              )
      # transpose the array to have the
      # shape = [n_samples, n_classifiers, n_classes]
      return np.array(list_proba).transpose((1, 0, 2))
  
imports: import numpy
  from scipy.stats.mstats import mode
  from sklearn.utils.validation import check_array
  from deslib.util.prob_functions import softmax
label_desc: Use image node_id 8 for calling a global function with example usage: _get_ensemble_probabilities(classifier_ensemble, X, estimator_features) and returns: np