input: Write a python function to analyze the sentiment of a sentence and label it as "positive", "negative", or "neutral" using nltk's SentimentIntensityAnalyzer.
  assert analyze_sentiment("This movie was extremely disappointing.") == "negative"
  assert analyze_sentiment("I absolutely loved the new design!") == "positive"
  assert analyze_sentiment("It was an average day.") == "neutral"
output: def analyze_sentiment(sentence):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      score = sia.polarity_scores(sentence)
      if score['compound'] >= 0.05:
          return "positive"
      elif score['compound'] <= -0.05:
          return "negative"
      else:
          return "neutral"