input: 
  from nltk.sentiment import SentimentIntensityAnalyzer
  
  def classify_sentiment(review: str) -> str:
      """Classify the sentiment of a given movie review using Natural Language Processing.
      The function uses VADER (Valence Aware Dictionary and Sentiment Reasoner) from the 
      NLTK library to analyze the sentiment of the review. It returns a string indicating 
      whether the sentiment of the review is positive or negative.
  
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'The sentiment of the sentence is negative!'
      """
      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:     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!'