input: Write a python function to determine the sentiment of a given sentence using NLTK's VADER sentiment analyzer.
  assert sentiment_analysis("The movie was so amazing!") == 'positive'
  assert sentiment_analysis("I didn't like the food.") == 'negative'
  assert sentiment_analysis("It was an okay experience.") == 'neutral'
output: def sentiment_analysis(sentence):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      import nltk
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(sentence)
      
      if scores['compound'] >= 0.05:
          return 'positive'
      elif scores['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'