input: def classify_sentiment(review: str) -> str:
      """ Classify the sentiment of a movie review using VADER Sentiment Analysis from NLTK.
      The function will return 'positive' if the sentiment is positive and 'negative' otherwise.
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      'negative'
      >>> classify_sentiment('The movie was amazing! I loved every minute of it.')
      'positive'
      """
      nltk.download('vader_lexicon', quiet=True)
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'
output:     import nltk
      from nltk.sentiment import vader
      nltk.download('vader_lexicon', quiet=True)
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'