input: def classify_sentiment(review: str) -> str:
      """ This function takes a movie review as input and uses Natural Language Processing
      to classify the sentiment as either positive or negative. It uses the VADER (Valence Aware Dictionary and sEntiment Reasoner) 
      from the nltk library to perform the sentiment analysis.
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'The sentiment of the sentence is negative!'
      """
      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:     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!'