input: from typing import List
  
  
  def classify_sentiment(sentences: List[str]) -> List[str]:
      """ Classify the sentiment of a list of movie reviews using Natural Language Processing.
      The sentiment analysis is performed using the VADER (Valence Aware Dictionary and sEntiment Reasoner) 
      tool from the NLTK library. It returns a list of strings where each string corresponds to the sentiment 
      of the respective movie review.
      
      >>> 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 plot was confusing."])
      ['The sentiment of the sentence is positive!', 'The sentiment of the sentence is negative!']
      """
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
      
      for sentence in sentences:
          scores = sid.polarity_scores(sentence)
          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 sentence in sentences:
          scores = sid.polarity_scores(sentence)
          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