input: from typing import List
  
  def classify_movie_review_sentiment(reviews: List[str]) -> List[str]:
      """ Uses a Sentiment Intensity Analyzer to classify the sentiment of a list of movie reviews.
      It returns a list of strings indicating whether the sentiment of each review is positive or negative.
      >>> classify_movie_review_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['The sentiment of the sentence is negative!']
      >>> classify_movie_review_sentiment(['The movie was fantastic and exceeded all my expectations!'])
      ['The sentiment of the sentence is positive!']
      """
  
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      results = []
  
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              results.append('The sentiment of the sentence is negative!')
          else:
              results.append('The sentiment of the sentence is positive!')
      
      return results
output:     from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      results = []
  
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              results.append('The sentiment of the sentence is negative!')
          else:
              results.append('The sentiment of the sentence is positive!')
      
      return results