input: 
  def classify_sentiment(review: str) -> str:
      """ Classify the sentiment of a movie review using VADER (Valence Aware Dictionary and sEntiment Reasoner)
      from the Natural Language Toolkit.
  
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'The sentiment of the sentence is negative!'
  
      >>> classify_sentiment('What an amazing film! The acting was superb.')
      'The sentiment of the sentence is positive!'
      """
  
output:     from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      sentiment_score = sia.polarity_scores(review)['compound']
      
      if sentiment_score >= 0.05:
          return 'The sentiment of the sentence is positive!'
      elif sentiment_score <= -0.05:
          return 'The sentiment of the sentence is negative!'
      else:
          return 'The sentiment of the sentence is neutral!'