input: 
  from typing import List
  import nltk
  from nltk.sentiment import vader
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using the VADER sentiment analysis tool. 
      Each review is classified as either 'positive' or 'negative' based on the sentiment scores.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!', 'It was the best movie ever!'])
      ['negative', 'positive']
      """
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('negative')
          else:
              sentiments.append('positive')
      return sentiments
  
output:     sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append('negative')
          else:
              sentiments.append('positive')
      return sentiments