input: Write a python function to analyze the sentiment of given sentences using NLTK's VADER SentimentIntensityAnalyzer.
  assert analyze_sentiment(["I am happy.", "I am not happy.", "This is great!"]) == [{'neg': 0.0, 'neu': 0.357, 'pos': 0.643, 'compound': 0.6249}, {'neg': 0.504, 'neu': 0.496, 'pos': 0.0, 'compound': -0.5859}, {'neg': 0.0, 'neu': 0.312, 'pos': 0.688, 'compound': 0.6249}]
  assert analyze_sentiment(["This is bad.", "I love this!", "I hate this."]) == [{'neg': 0.625, 'neu': 0.375, 'pos': 0.0, 'compound': -0.5423}, {'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.6369}, {'neg': 0.639, 'neu': 0.361, 'pos': 0.0, 'compound': -0.5574}]
output: def analyze_sentiment(sentences):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      return [sia.polarity_scores(sentence) for sentence in sentences]