input: from typing import List
  
  def classify_sentiment(sentences: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie review sentences using VADER sentiment analysis.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'])
      ['The sentiment of the sentence is negative!']
      >>> classify_sentiment(['The movie was amazing. The effects were breathtaking!'])
      ['The sentiment of the sentence is positive!']
      """
      from nltk.sentiment import vader 
      sid = vader.SentimentIntensityAnalyzer()
      sentiment_labels = []
      
      for sentence in sentences:
          scores = sid.polarity_scores(sentence)
          if scores['neg'] > scores['pos']:
              sentiment_labels.append('The sentiment of the sentence is negative!')
          else:
              sentiment_labels.append('The sentiment of the sentence is positive!')
      
      return sentiment_labels
output:     from nltk.sentiment import vader 
      sid = vader.SentimentIntensityAnalyzer()
      sentiment_labels = []
      
      for sentence in sentences:
          scores = sid.polarity_scores(sentence)
          if scores['neg'] > scores['pos']:
              sentiment_labels.append('The sentiment of the sentence is negative!')
          else:
              sentiment_labels.append('The sentiment of the sentence is positive!')
      
      return sentiment_labels