input: Write a python function to classify the sentiment of a text as positive, neutral, or negative using NLTK's SentimentIntensityAnalyzer.
  assert classify_sentiment('The film was terrible') == 'negative'
  assert classify_sentiment('The movie was fantastic!') == 'positive'
  assert classify_sentiment('The event was okay') == 'neutral'
  assert classify_sentiment('I had an amazing time at the concert') == 'positive'
  assert classify_sentiment('It was a boring and dull performance') == 'negative'
output: def classify_sentiment(text):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      score = sia.polarity_scores(text)
      if score['compound'] >= 0.05:
          return 'positive'
      elif score['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'