input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should return 'negative' if the sentiment is more negative and 'positive' if the sentiment is more positive. Use the VADER sentiment analysis tool from NLTK.
  def classify_sentiment(review: str) -> str:
      import nltk
      from nltk.sentiment import vader
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      return 'negative' if scores['neg'] > scores['pos'] else 'positive'
  
  assert classify_sentiment('The movie was terrible. It felt like it would never end!') == 'negative'
  assert classify_sentiment('This film is an absolute masterpiece with stunning visuals and captivating storylines.') == 'positive'
  assert classify_sentiment('Overall, it was quite enjoyable.') == 'positive'
  assert classify_sentiment('I hated every minute of it.') == 'negative'
output: def classify_sentiment(review: str) -> str:
      import nltk
      from nltk.sentiment import vader
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      return 'negative' if scores['neg'] > scores['pos'] else 'positive'