code: def sum_votes_per_class(predictions, n_classes):
      """Sum the number of votes for each class. Accepts masked arrays as input.
  
      Parameters
      ----------
      predictions : array of shape (n_samples, n_classifiers),
          The votes obtained by each classifier for each sample. Can be a masked
          array.
  
      n_classes : int
          Number of classes.
  
      Returns
      -------
      summed_votes : array of shape (n_samples, n_classes)
          Summation of votes for each class
      """
      votes = np.zeros((predictions.shape[0], n_classes), dtype=int)
      for label in range(n_classes):
          votes[:, label] = np.sum(predictions == label, axis=1)
      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 7 for calling a global function with example usage: sum_votes_per_class(predictions, n_classes) and returns: votes