input: 
  def naive_bayes_classifier_for_spam_detection(emails: List[str], labels: List[str]) -> str:
      """Implement a naive bayes classifier for spam detection. The function should 
      take a list of emails and their corresponding labels ('spam' or 'not spam') as 
      input. It should train a Gaussian Naive Bayes classifier on the provided data 
      and return a string that describes the classifier and its prediction.
      
      >>> naive_bayes_classifier_for_spam_detection(
          ["Free money!", "Let's grab lunch tomorrow", "You have won a $1000 gift card!"], 
          ["spam", "not spam", "spam"]
      )
      "Classifier initialized and trained. Predicted output: ['spam', 'not spam', 'spam']."
      """
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails).toarray()
  
      # Train the classifier
      classifier = GaussianNB()
      classifier.fit(X, labels)
  
      # Predict the labels for the training data
      predictions = classifier.predict(X)
  
      # Return the description of the classifier and its predictions
      return f"Classifier initialized and trained. Predicted output: {list(predictions)}."