new_contents: import numpy as np
  import cv2
  
  # pos and neg are positive and negative instances
  # each is a list of files of nparray dumps,
  # nparray of BoW histograms; shape = (n, 101)
  # of the class to be trained for
  def build_trained_classifier(pos_files, neg_files):
      total = len(pos_files) + len(neg_files)
      samples = np.empty((total, 101), np.float32)
  
      i = 0
      for pos_file in pos_files:
          samples[i] = np.load(pos_file)
          i = i + 1
      for neg_file in neg_files:
          samples[i] = np.load(neg_file)
          i = i + 1
  
      labels = np.empty((total, 1), np.float32)
      labels[0:len(pos_files), 0] = 1.0
      labels[len(pos_files):, 0] = 0.0
  
      return cv2.NormalBayesClassifier(samples, labels)
  
  
subject: Implement Naive Bayes Classifier builder method