input: def naive_bayes_classifier_for_spam(emails: List[str], labels: List[int]) -> GaussianNB:
      """ Constructs a Naive Bayes classifier to classify emails as spam or not spam. 
      The function uses the GaussianNB classifier from sklearn. It trains the model 
      on the provided emails and labels, and then predicts the classification on 
      new email data.
      >>> emails = ["Get rich quick!", "Make money fast!", "Free investment seminar"]
      >>> labels = [1, 1, 1]
      >>> X_test = ["Earn extra income!"]
      >>> model = naive_bayes_classifier_for_spam(emails, labels)
      >>> y_pred = model.predict(X_test)
      >>> y_pred[0]
      1
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails).toarray()
      y = labels
  
      model = GaussianNB()
      model.fit(X, y)
  
      return model