code: def majority_voting(classifier_ensemble, X):
      """Apply the majority voting rule to predict the label of 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.
  
      Returns
      -------
      predicted_label : array of shape (n_samples)
          The label of each query sample predicted using the majority voting rule
      """
      votes = _get_ensemble_votes(classifier_ensemble, X)
      predicted_label = majority_voting_rule(votes)
  
      return predicted_label
  
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 1 for calling a global function with example usage: majority_voting(classifier_ensemble, X) and returns: predicted_label