input: from typing import List
  
  def classify_movie_reviews(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using Natural Language Processing (NLP)
      and the VADER sentiment analysis tool from the NLTK library.
      >>> classify_movie_reviews(["The movie was terrible. It felt like it would never end!"])
      ['The sentiment of the sentence is negative!']
      >>> classify_movie_reviews(["The movie was fantastic! The storyline kept me engaged."])
      ['The sentiment of the sentence is positive!']
      """
      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('The sentiment of the sentence is negative!')
          else:
              sentiments.append('The sentiment of the sentence is 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('The sentiment of the sentence is negative!')
          else:
              sentiments.append('The sentiment of the sentence is positive!')
      return sentiments