input: Write a function to classify an email message using GaussianNB classifier.
  assert classify_email('Get Rich Quick!') == 'spam'
  assert classify_email('Earn money faster than you imagined') == 'spam'
  assert classify_email('Have a great day') == 'not spam'
  assert classify_email('Quick meeting at 10') == 'not spam'
output: def classify_email(message):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import GaussianNB
      import numpy as np
      
      # Training data
      emails = [
          'Get Rich Quick!',
          'Earn money faster than you imagined',
          'Have a great day',
          'Quick meeting at 10'
      ]
      labels = ['spam', 'spam', 'not spam', 'not spam']
      
      # Convert emails to feature vectors
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails).toarray()
      
      # Train the GaussianNB classifier
      clf = GaussianNB()
      clf.fit(X, labels)
      
      # Transform the input message to the feature vector
      message_vector = vectorizer.transform([message]).toarray()
      
      # Predict the class of the input message
      prediction = clf.predict(message_vector)
      
      return prediction[0]