input: Write a python function to analyze the sentiment of a given sentence using NLTK's Vader SentimentIntensityAnalyzer.
  assert analyze_sentiment('The movie was terrible. It felt like it would never end!') == 'negative'
  assert analyze_sentiment('I loved the movie! It was fantastic and thrilling.') == 'positive'
  assert analyze_sentiment('The movie was okay, not great but not bad either.') == 'positive'
output: def analyze_sentiment(sentence):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      from nltk import download
      download('vader_lexicon')
  
      sia = SentimentIntensityAnalyzer()
      scores = sia.polarity_scores(sentence)
      
      if scores['compound'] >= 0.05:
          return 'positive'
      elif scores['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'