input: 
  def analyze_sentiment(text):
      """Analyzes the sentiment of the given text and categorizes it as positive, negative, or neutral.
      The sentiment analysis is based on the results from NLTK's NaiveBayesClassifier, which may need to be trained
      before being used in this function.
      
      >>> analyze_sentiment("I absolutely love this product! Highly recommended.")
      'positive'
      >>> analyze_sentiment("I hate waiting in line.")
      'negative'
      >>> analyze_sentiment("The weather is fine today.")
      'neutral'
      """
  import nltk
  from nltk.tokenize import sent_tokenize
  from nltk.classify import NaiveBayesClassifier
  
  def analyze_sentiment(text):
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          if sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment
  
  # End analyze sentiment definition
  
output:     if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment