input: Write a python function to classify sentiment using NLTK's VADER sentiment analyzer.
  assert classify_sentiment("I love the new design of the website!") == 'positive'
  assert classify_sentiment("I am very disappointed with the customer service.") == 'negative'
  assert classify_sentiment("The product is okay, not too bad.") == 'neutral'
output: def classify_sentiment(text):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(text)
      
      if scores['compound'] >= 0.05:
          return 'positive'
      elif scores['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'