input: from typing import List, Tuple
  
  def classify_emails(training_data: List[Tuple[List[float], int]], test_data: List[List[float]]) -> List[int]:
      """ Construct a naive bayes classifier using Python to classify emails as spam or not spam.
      The training data is a list of tuples, where each tuple contains a list of feature values and 
      the corresponding label (0 for not spam, 1 for spam). The test data is a list of lists, where 
      each list contains feature values for an email. The function should return a list of labels,
      where each label corresponds to the prediction for the corresponding email in the test data.
      
      >>> classify_emails([([0.1, 0.2, 0.3], 0), ([0.4, 0.5, 0.6], 1)], [[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]])
      [0, 1]
      """
output:     # Initialize variables
      num_spam = 0
      num_not_spam = 0
      feature_sum_spam = [0.0] * len(training_data[0][0])
      feature_sum_not_spam = [0.0] * len(training_data[0][0])
  
      # Calculate the number of spam and not spam emails and sum of features for each class
      for features, label in training_data:
          if label == 1:
              num_spam += 1
              feature_sum_spam = [sum(x) for x in zip(feature_sum_spam, features)]
          else:
              num_not_spam += 1
              feature_sum_not_spam = [sum(x) for x in zip(feature_sum_not_spam, features)]
  
      # Calculate the mean feature values for spam and not spam
      mean_spam = [x / num_spam for x in feature_sum_spam]
      mean_not_spam = [x / num_not_spam for x in feature_sum_not_spam]
  
      # Calculate the prior probabilities
      total_emails = num_spam + num_not_spam
      p_spam = num_spam / total_emails
      p_not_spam = num_not_spam / total_emails
  
      # Classify the test data
      predictions = []
      for features in test_data:
          # Calculate the likelihoods
          likelihood_spam = 1.0
          likelihood_not_spam = 1.0
          for i in range(len(features)):
              likelihood_spam *= (1 / (mean_spam[i] * (2 * 3.14159) ** 0.5)) * \
                                  (2.71828 ** (-((features[i] - mean_spam[i]) ** 2) / (2 * mean_spam[i] ** 2)))
              likelihood_not_spam *= (1 / (mean_not_spam[i] * (2 * 3.14159) ** 0.5)) * \
                                      (2.71828 ** (-((features[i] - mean_not_spam[i]) ** 2) / (2 * mean_not_spam[i] ** 2)))
  
          # Calculate the posterior probabilities
          p_features_given_spam = likelihood_spam * p_spam
          p_features_given_not_spam = likelihood_not_spam * p_not_spam
  
          # Classify based on the highest posterior probability
          if p_features_given_spam > p_features_given_not_spam:
              predictions.append(1)
          else:
              predictions.append(0)
  
      return predictions