input: Write a function to construct a naive bayes classifier using python to classify emails as spam or not spam. The function should create a Gaussian Classifier, train it using the training sets, and predict the output for the test set. If the given training and testing data are empty, the function should return None.
  
  from sklearn.naive_bayes import GaussianNB
  
  def naive_bayes_classifier(X_train, y_train, X_test):
      if not X_train or not X_test:
          return None
      
      # 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
  
  assert list(naive_bayes_classifier([[0], [1]], [0, 1], [[2], [3]]))==[0, 0]
  assert naive_bayes_classifier([], [], [])==None
  assert list(naive_bayes_classifier([[0], [1]], [0, 1], []))==None
  assert list(naive_bayes_classifier([], [], [[2], [3]]))==None
  assert list(naive_bayes_classifier([[0], [1]], [0, 1], [[0.5], [2]]))==[0, 1]
output: def naive_bayes_classifier(X_train, y_train, X_test):
      if not X_train or not X_test:
          return None
      
      model = GaussianNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return y_pred