input: from typing import List
  
  def classify_emails(training_features: List[List[float]], training_labels: List[int], test_features: List[List[float]]) -> List[int]:
      """ Classify emails as spam or not spam using a naive bayes classifier. The input consists of training features, training labels, and test features.
      The output is a list of predicted labels for the test features.
      >>> classify_emails([[1.0, 2.0], [3.0, 4.0]], [0, 1], [[1.5, 2.5]])
      [0]
      >>> classify_emails([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [0, 1, 0], [[1.5, 2.5], [4.5, 5.5]])
      [0, 0]
      """
output:     from sklearn.naive_bayes import GaussianNB
      from sklearn.metrics import accuracy_score
  
      # Initialize the Naive Bayes classifier
      classifier = GaussianNB()
  
      # Train the classifier with the training data
      classifier.fit(training_features, training_labels)
  
      # Predict the labels for the test features
      predicted_labels = classifier.predict(test_features)
  
      return predicted_labels