input: Write a python function to analyze the sentiment of a given text using NLTK's VADER and return a dictionary of sentiment scores.
  assert analyze_sentiment("That movie was so great") == {'neg': 0.0, 'neu': 0.364, 'pos': 0.636, 'compound': 0.7096}
  assert analyze_sentiment("The plot was terrible and the characters were boring") == {'neg': 0.406, 'neu': 0.594, 'pos': 0.0, 'compound': -0.7269}
  assert analyze_sentiment("I have no strong feelings one way or the other") == {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
output: def analyze_sentiment(text):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      nltk.download('vader_lexicon')
      sid = SentimentIntensityAnalyzer()
      return sid.polarity_scores(text)