input: 
  from typing import List, Tuple
  
  def classify_sentiment(reviews: List[str]) -> List[Tuple[str, str]]:
      """Classify the sentiment of a list of movie reviews using Natural Language Processing (NLP).
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!', 'This is the best movie I have ever seen!', 'Meh. It was okay.'])
      [('The movie was terrible. It felt like it would never end!', 'negative'), ('This is the best movie I have ever seen!', 'positive'), ('Meh. It was okay.', 'neutral')]
      """
      nltk.download('vader_lexicon')
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append((review, 'negative'))
          elif scores['pos'] > scores['neg']:
              sentiments.append((review, 'positive'))
          else:
              sentiments.append((review, 'neutral'))
      return sentiments
  
output:     import nltk
      from nltk.sentiment import vader
      nltk.download('vader_lexicon')
      sid = vader.SentimentIntensityAnalyzer()
      sentiments = []
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['neg'] > scores['pos']:
              sentiments.append((review, 'negative'))
          elif scores['pos'] > scores['neg']:
              sentiments.append((review, 'positive'))
          else:
              sentiments.append((review, 'neutral'))
      return sentiments