input: 
  from typing import Tuple
  import nltk
  from nltk.sentiment import vader
  
  def classify_sentiment(review: str) -> Tuple[str, float, float, float]:
      """ Classifies the sentiment of a given movie review using the VADER sentiment analysis tool.
      Returns a tuple containing:
      1. The classified sentiment label ('positive' or 'negative')
      2. The negative score
      3. The neutral score
      4. The positive score
      >>> classify_sentiment('The movie was terrible. It felt like it would never end!')
      ('negative', 0.446, 0.554, 0.0)
      """
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          sentiment = 'negative'
      else:
          sentiment = 'positive'
      return sentiment, scores['neg'], scores['neu'], scores['pos']
  
output:     sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          sentiment = 'negative'
      else:
          sentiment = 'positive'
      return sentiment, scores['neg'], scores['neu'], scores['pos']