input: Write a python function to classify the sentiment of a given text as 'positive', 'negative', or 'neutral'.
  assert classify_sentiment(nltk.word_tokenize("The airport experience was not bad at all.")) in ['positive', 'negative', 'neutral']
  assert classify_sentiment(nltk.word_tokenize("I had a terrible flight.")) in ['positive', 'negative', 'neutral']
  assert classify_sentiment(nltk.word_tokenize("It was an okay trip.")) in ['positive', 'negative', 'neutral']
output: def classify_sentiment(tokens):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      scores = sia.polarity_scores(' '.join(tokens))
      compound_score = scores['compound']
      
      if compound_score >= 0.05:
          return 'positive'
      elif compound_score <= -0.05:
          return 'negative'
      else:
          return 'neutral'