input: from typing import List
  
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Given a list of movie reviews, classify each review as either 'positive' or 'negative'
      using the VADER sentiment analysis tool.
      
      >>> classify_sentiment(["The movie was terrible. It felt like it would never end!"])
      ['negative']
      >>> classify_sentiment(["The film was amazing and the actors performed brilliantly.", "I didn't like the ending at all."])
      ['positive', 'negative']
      """
      
      from nltk.sentiment import vader
      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:     from nltk.sentiment import vader
      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