input: Write a python function to analyze the sentiment of a given sentence using nltk's SentimentIntensityAnalyzer.
  assert sentiment_analyzer('This movie was great!')=='Positive'
  assert sentiment_analyzer('This movie was okay.')=='Neutral'
  assert sentiment_analyzer('This movie was terrible!')=='Negative'
output: def sentiment_analyzer(sentence):
      from nltk.sentiment.vader 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'