code: def _vote_disagreement(self, votes):
          
          ret = []
          for candidate in votes:
              ret.append(0.0)
              lab_count = {}
              for lab in candidate:
                  lab_count[lab] = lab_count.setdefault(lab, 0) + 1
  
              # Using vote entropy to measure disagreement
              for lab in lab_count.keys():
                  ret[-1] -= lab_count[lab] / self.n_students * \
                      math.log(float(lab_count[lab]) / self.n_students)
  
          return ret
docs: Return the disagreement measurement of the given number of votes.
          It uses the vote vote to measure the disagreement.
  
          Parameters
          ----------
          votes : list of int, shape==(n_samples, n_students)
              The predictions that each student gives to each sample.
  
          Returns
          -------
          disagreement : list of float, shape=(n_samples)
              The vote entropy of the given votes.