input: Write a python function to analyze the sentiment of a given text using NLTK's VADER sentiment analyzer.
  assert analyze_sentiment("The product was amazing and I highly recommend it!") == "positive"
  assert analyze_sentiment("I had a terrible experience with this service.") == "negative"
  assert analyze_sentiment("The movie was okay, not great but not bad either.") == "neutral"
output: def analyze_sentiment(text):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      import nltk
      nltk.download('vader_lexicon')
      
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(text)
      
      if sentiment_scores['compound'] >= 0.05:
          return "positive"
      elif sentiment_scores['compound'] <= -0.05:
          return "negative"
      else:
          return "neutral"