input: Write a python function to perform sentiment analysis on a list of sentences using nltk's SentimentIntensityAnalyzer.
  assert analyze_sentiment(["The food was absolutely delicious."]) == [{'neg': 0.0, 'neu': 0.327, 'pos': 0.673, 'compound': 0.6696}]
  assert analyze_sentiment(["I love this place!", "I am not happy with the service."]) == [{'neg': 0.0, 'neu': 0.304, 'pos': 0.696, 'compound': 0.6696}, {'neg': 0.582, 'neu': 0.418, 'pos': 0.0, 'compound': -0.4767}]
  assert analyze_sentiment(["This is a test sentence.", "Another test."]) == [{'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.4404}, {'neg': 0.0, 'neu': 0.68, 'pos': 0.32, 'compound': 0.3612}]
output: def analyze_sentiment(sentences):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      return [sia.polarity_scores(sentence) for sentence in sentences]