input: 
  def classify_string(input_string: str) -> str:
      """
      Given an input string, this function uses a pre-trained Multinomial Naive Bayes classifier
      to categorize the input string into one of the predefined classes.
  
      >>> classify_string("This is a positive review")
      'positive'
      >>> classify_string("I am not happy with the service")
      'negative'
      """
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Pre-trained classifier and vectorizer
      classifier = MultinomialNB()
      vectorizer = CountVectorizer()
  
      # Example training data
      train_data = ["This is a positive review", "I am not happy with the service", "The service was excellent", "I had a bad experience"]
      train_labels = ["positive", "negative", "positive", "negative"]
  
      # Fit the vectorizer and classifier
      vectorizer.fit(train_data)
      train_vectors = vectorizer.transform(train_data)
      classifier.fit(train_vectors, train_labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the class
      predicted_class = classifier.predict(input_vector)
  
      return predicted_class[0]