input: Write a python function to analyze the sentiment of a list of tweets using the SentimentIntensityAnalyzer from the nltk library.
  assert analyze_sentiment(["I am so happy today!", "I am feeling very sad right now"]) == [{'neg': 0.0, 'neu': 0.328, 'pos': 0.672, 'compound': 0.8519}, {'neg': 0.736, 'neu': 0.264, 'pos': 0.0, 'compound': -0.6908}]
  assert analyze_sentiment(["This is the best day ever.", "This is the worst day ever."]) == [{'neg': 0.0, 'neu': 0.511, 'pos': 0.489, 'compound': 0.7096}, {'neg': 0.706, 'neu': 0.294, 'pos': 0.0, 'compound': -0.743}]
  assert analyze_sentiment(["I love this!", "I hate this!"]) == [{'neg': 0.0, 'neu': 0.327, 'pos': 0.673, 'compound': 0.6696}, {'neg': 0.684, 'neu': 0.316, 'pos': 0.0, 'compound': -0.6486}]
output: def analyze_sentiment(tweets):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      sid = SentimentIntensityAnalyzer()
      return [sid.polarity_scores(tweet) for tweet in tweets]