input: 
  def classify_sentiment(movie_review: str) -> str:
      """
      Classify the sentiment of a movie review using VADER sentiment analysis.
      It will return 'negative' if the negative sentiment score is higher, otherwise
      it will return 'positive'.
  
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'negative'
      >>> classify_sentiment('It was an amazing film. Full of suspense and drama.')
      'positive'
      """
      import nltk
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(movie_review)
  
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'
  
output:     import nltk
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(movie_review)
  
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'