input: def classify_sentiment(review: str) -> str:
      """ Classify the sentiment of a movie review using VADER sentiment analysis.
      >>> classify_sentiment("The movie was terrible. It felt like it would never end!")
      'The sentiment of the sentence is negative!'
      >>> classify_sentiment("The movie was amazing. I loved every minute of it!")
      'The sentiment of the sentence is positive!'
      """
      
  import nltk
  from nltk.sentiment import SentimentIntensityAnalyzer
  
  sid = SentimentIntensityAnalyzer()
  scores = sid.polarity_scores(review)
  
  if scores['neg'] > scores['pos']:
      return 'The sentiment of the sentence is negative!'
  else:
      return 'The sentiment of the sentence is positive!'
output:     import nltk
      from nltk.sentiment import SentimentIntensityAnalyzer
  
      sid = SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
  
      if scores['neg'] > scores['pos']:
          return 'The sentiment of the sentence is negative!'
      else:
          return 'The sentiment of the sentence is positive!'