input: from typing import List
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of given movie reviews using Vader sentiment analysis.
      The function will analyze each review and return a list of sentiments corresponding
      to the input list of reviews. The sentiment will be 'negative' if the negative score
      is higher and 'positive' if the positive score is higher.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['negative']
      >>> classify_sentiment(['I loved the movie. It was fantastic!', 'Boring and uninteresting.'])
      ['positive', 'negative']
      """
      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