input: Write a function to construct a naive Bayes classifier using Python to classify emails as spam or not spam. The function should initialize the classifier, train it using provided training data, and then predict the classification for test data. If the training or test data is empty, return an empty list.
  assert construct_naive_bayes_classifier([[1, 0], [0, 1]], [0, 1], [[1, 1], [0, 0]])==[1, 0]
  assert construct_naive_bayes_classifier([], [], [])==[]
  assert construct_naive_bayes_classifier([[1, 0], [0, 1]], [1, 0], [[1, 1], [0, 0]])==[0, 1]
  assert construct_naive_bayes_classifier([[1, 0]], [0], [])==[]
  assert construct_naive_bayes_classifier([[1, 0]], [1], [[1, 1]])==[1]
output: def construct_naive_bayes_classifier(train_data, train_labels, test_data):
      if not train_data or not test_data:
          return []
      
      from sklearn.naive_bayes import GaussianNB
      classifier = GaussianNB()
      classifier.fit(train_data, train_labels)
      return classifier.predict(test_data).tolist()