input: 
  def naive_bayes_classifier(X_train, y_train, X_test):
      """
      Constructs a Naive Bayes classifier using GaussianNB from sklearn
      and returns the predicted labels for the test data.
      
      >>> X_train = [[0, 0], [1, 1]]
      >>> y_train = [0, 1]
      >>> X_test = [[2., 2.], [-2., -1.]]
      >>> naive_bayes_classifier(X_train, y_train, X_test)
      [1, 0]
      """
      from sklearn.naive_bayes import GaussianNB
      
      # 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:     from sklearn.naive_bayes import GaussianNB
      
      # 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