class_docstr: K Nearest Neighbors classifier.
  
  Parameters:
  -----------
  k: int
      The number of closest neighbors that will determine the class of the 
      sample that we wish to predict.
docstr: Return the most common class among the neighbor samples 
docstr_tok: ["Return","the","most","common","class","among","the","neighbor","samples"]
code: def _vote(self, neighbor_labels):
      """Return the most common class among the neighbor samples"""
      counts = np.bincount(neighbor_labels.astype("int"))
      return counts.argmax()
  
imports: from __future__ import print_function, division
  import numpy
  from mlfromscratch.utils import euclidean_distance
label_desc: Use image node_id 2 for calling the KNN obj's underlying member method code with example usage: obj._vote(neighbor_labels) and returns: counts