input: from typing import List
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using VADER sentiment analysis. 
      The VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based
      sentiment analysis tool. The function will return a list of strings indicating whether
      each review is classified as 'positive' or 'negative'. If the negative score is higher,
      the review is classified as 'negative'; otherwise, it is classified as 'positive'.
      
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['negative']
      >>> classify_sentiment(['The acting was fantastic, and the story was engaging.'])
      ['positive']
      """
      nltk.download('vader_lexicon')
      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:     nltk.download('vader_lexicon')
      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