input: Write a python function to analyze sentiment of a given text using NLTK's VADER sentiment analyzer.
  assert analyze_sentiment('This restaurant is terrible.')=='negative'
  assert analyze_sentiment('I absolutely love this place!')=='positive'
  assert analyze_sentiment('The food was okay, but the service was excellent.')=='positive'
output: def analyze_sentiment(text):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      import nltk
      nltk.download('vader_lexicon')
      
      sid = SentimentIntensityAnalyzer()
      sentiment_scores = sid.polarity_scores(text)
      
      if sentiment_scores['compound'] >= 0.05:
          return 'positive'
      elif sentiment_scores['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'