code: def _majority_vote(self, y):
      most_common = None
      max_count = 0
      for label in np.unique(y):
          # Count number of occurences of samples with label
          count = len(y[y == label])
          if count > max_count:
              most_common = label
              max_count = count
      return most_common
  
imports: from __future__ import division, print_function
  import numpy
  from mlfromscratch.utils import divide_on_feature, train_test_split, standardize, mean_squared_error
  from mlfromscratch.utils import calculate_entropy, accuracy_score, calculate_variance
label_desc: Use image node_id 2 for calling the ClassificationTree obj's underlying member method code with example usage: obj._majority_vote(y) and returns: most_common