code: # Import the necessary modules
  from sklearn.naive_bayes import MultinomialNB
  from sklearn import metrics
  
  # Instantiate a Multinomial Naive Bayes classifier: nb_classifier
  nb_classifier = MultinomialNB()
  
  # Fit the classifier to the training data
  nb_classifier.fit(count_train, y_train)
  
  # Create the predicted tags: pred
  pred = nb_classifier.predict(count_test)
  
  # Calculate the accuracy score: score
  score = metrics.accuracy_score(y_test, pred)
  print(score)
  
  # Calculate the confusion matrix: cm
  cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])
  print(cm)
apis: sklearn.naive_bayes.MultinomialNB sklearn.metrics.accuracy_score sklearn.metrics.confusion_matrix