input: 
  from typing import List
  
  
  def classify_sentiment(reviews: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using Natural Language Processing and the VADER sentiment analyzer.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['The sentiment of the sentence is negative!']
      >>> classify_sentiment(['What an amazing film, everyone should watch it!', 'The acting was awful, I could not enjoy the film.'])
      ['The sentiment of the sentence is positive!', 'The sentiment of the sentence is negative!']
      """
      from nltk.sentiment import SentimentIntensityAnalyzer
      
      sid = 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 SentimentIntensityAnalyzer
      
      sid = 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