input: Write a python function to analyze the sentiment of given sentences using NLTK's SentimentIntensityAnalyzer. The function should return the sentiment type ("positive", "negative", or "neutral") based on the compound score.
  assert analyze_sentiment(["This product is amazing!"]) == ["positive"]
  assert analyze_sentiment(["I really dislike this service."]) == ["negative"]
  assert analyze_sentiment(["This product is mediocre."]) == ["neutral"]
  assert analyze_sentiment(["I'm not sure how I feel about this."]) == ["neutral"]
  assert analyze_sentiment(["Best purchase ever!", "Total waste of money."]) == ["positive", "negative"]
output: def analyze_sentiment(sentences):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      
      results = []
      for sentence in sentences:
          scores = sia.polarity_scores(sentence)
          compound = scores['compound']
          if compound >= 0.05:
              sentiment = "positive"
          elif compound <= -0.05:
              sentiment = "negative"
          else:
              sentiment = "neutral"
          results.append(sentiment)
      
      return results