input: Write a python function to classify emails using TfidfVectorizer and MultinomialNB.
  assert classify_emails(["Hello, how are you?", "Cheap loans available now"], ["ham", "spam"], ["Free money now"])==["spam"]
  assert classify_emails(["Meeting at 10 AM", "Don't miss our sale"], ["ham", "spam"], ["Important meeting tomorrow"])==["ham"]
  assert classify_emails(["Discount offers for you", "Your order is shipped"], ["spam", "ham"], ["Special discount just for you"])==["spam"]
output: def classify_emails(training_emails, training_labels, new_emails):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Create a TfidfVectorizer
      vectorizer = TfidfVectorizer()
  
      # Fit and transform the training emails
      X_train = vectorizer.fit_transform(training_emails)
  
      # Initialize the Multinomial Naive Bayes classifier
      clf = MultinomialNB()
  
      # Fit the classifier on the training data
      clf.fit(X_train, training_labels)
  
      # Transform the new emails using the same vectorizer
      X_new = vectorizer.transform(new_emails)
  
      # Predict the labels for the new emails
      predictions = clf.predict(X_new)
  
      return list(predictions)