input: 
  from sklearn.naive_bayes import GaussianNB
  
  def classify_emails(X_train, y_train, X_test):
      """
      Construct a naive bayes classifier to classify emails as spam or not spam using GaussianNB.
      The function trains the model on the provided training data and predicts the labels for 
      the test data.
      
      >>> X_train = [[1, 'hi', 'formal'], [2, 'discount', 'financial'], [3, 'hello', 'personal']]
      >>> y_train = [0, 1, 0]
      >>> X_test = [[4, 'sale', 'financial'], [5, 'greetings', 'personal']]
      >>> classify_emails(X_train, y_train, X_test)
      [1, 0]
      """
      # Create a Gaussian Classifier
      model = GaussianNB()
      
      # Train the model using the training sets 
      model.fit(X_train, y_train)
      
      # Predict Output 
      y_pred = model.predict(X_test)
      return y_pred
  
output:     # Create a Gaussian Classifier
      model = GaussianNB()
      
      # Train the model using the training sets 
      model.fit(X_train, y_train)
      
      # Predict Output 
      y_pred = model.predict(X_test)
      return y_pred