input: Write a python function to analyze sentiment of a list of sentences using VADER sentiment analysis.
  assert analyze_sentiment(["This is a great movie!"]) == [{'neg': 0.0, 'neu': 0.314, 'pos': 0.686, 'compound': 0.6588}]
  assert analyze_sentiment(["This movie was terrible."]) == [{'neg': 0.453, 'neu': 0.547, 'pos': 0.0, 'compound': -0.4767}]
  assert analyze_sentiment(["I love this!", "I hate this!"]) == [{'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.6369}, {'neg': 0.594, 'neu': 0.406, 'pos': 0.0, 'compound': -0.6696}]
output: def analyze_sentiment(sentences):
      from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
  
      analyzer = SentimentIntensityAnalyzer()
      results = []
  
      for sentence in sentences:
          sentiment = analyzer.polarity_scores(sentence)
          results.append(sentiment)
      
      return results