code: def predict_proba_ensemble(
      classifier_ensemble, X, estimator_features=None
  ):
      """Estimates the posterior probabilities of the give ensemble for each
      sample in X.
  
      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
      -------
      predicted_proba : array of shape (n_samples, n_classes)
          Posterior probabilities estimates for each samples in X.
      """
      ensemble_proba = _get_ensemble_probabilities(
          classifier_ensemble, X, estimator_features
      )
      n_classifiers = ensemble_proba.shape[1]
      predicted_proba = np.sum(ensemble_proba, axis=1) / n_classifiers
      return predicted_proba
  
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 9 for calling a global function with example usage: predict_proba_ensemble(classifier_ensemble, X, estimator_features) and returns: predicted_proba