input: Write a python function to classify new tweets using a pre-trained MultinomialNB model.
  assert classify_tweets(["You are so stupid", "I totally hate you"]) == [1, 1]
  assert classify_tweets(["I love sunny days", "This is amazing"]) == [0, 0]
  assert classify_tweets(["I totally hate this", "You are amazing"]) == [1, 0]
output: def classify_tweets(tweets):
      import pickle
      from sklearn.feature_extraction.text import CountVectorizer
  
      # Load the pre-trained model and vectorizer
      with open('multinomial_nb_model.pkl', 'rb') as model_file:
          model = pickle.load(model_file)
          
      with open('count_vectorizer.pkl', 'rb') as vectorizer_file:
          vectorizer = pickle.load(vectorizer_file)
      
      # Transform the tweets using the loaded vectorizer
      tweet_vectors = vectorizer.transform(tweets)
      
      # Predict the classes for the tweets using the loaded model
      predictions = model.predict(tweet_vectors)
      
      return predictions.tolist()