code: def _get_ensemble_votes(classifier_ensemble, X):
      """Calculates the votes obtained by each based classifier in the ensemble
      for 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
      -------
      votes : array of shape (n_samples, n_classifiers)
              The votes obtained by each base classifier
      """
      # Check if a single sample was passed down to the function. In this case
      # the sample must be converted to a 2D array.
      X = check_array(X, ensure_2d=False)
      if X.ndim == 1:
          X = np.atleast_2d(X)
  
      n_samples = X.shape[0]
      votes = np.zeros((n_samples, len(classifier_ensemble)))
      for clf_index, clf in enumerate(classifier_ensemble):
          votes[:, clf_index] = clf.predict(X)
  
      return votes
  
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 3 for calling a global function with example usage: _get_ensemble_votes(classifier_ensemble, X) and returns: votes