input: def naive_bayes_classifier(emails: List[str], labels: List[str]) -> List[str]:
      """ Construct a naive bayes classifier using scikit-learn to classify emails as spam or not spam.
      >>> naive_bayes_classifier(['You won 10000$', 'Hello there'], ['spam', 'ham'])
      ['spam', 'ham']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Fit the model with the provided emails and labels
      model.fit(emails, labels)
  
      # Predict the labels for the input emails
      predicted_labels = model.predict(emails)
  
      return predicted_labels