input: Write a python function to classify the sentiment of a tweet using NLTK and VADER sentiment analysis.
  assert classify_tweet("I am really enjoying this course!") == 'Positive'
  assert classify_tweet("I am not happy with the service.") == 'Negative'
  assert classify_tweet("This is an average product.") == 'Neutral'
output: def classify_tweet(tweet):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      import nltk
      nltk.download('vader_lexicon')
      
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(tweet)
      
      if sentiment_scores['compound'] >= 0.05:
          return 'Positive'
      elif sentiment_scores['compound'] <= -0.05:
          return 'Negative'
      else:
          return 'Neutral'