input: Write a function to analyze the sentiment of a given text, categorizing it into positive, negative, or neutral sentiments. Use the NaiveBayesClassifier from the nltk.classify module for sentiment analysis. The function should tokenize the text into sentences, analyze each sentence's sentiment, and return the overall sentiment of the text. If the number of negative sentences is greater than the number of positive sentences, the sentiment should be categorized as negative. If the number of positive sentences is greater than the number of negative sentences, the sentiment should be categorized as positive. If the number of positive and negative sentences is equal, the sentiment should be categorized as neutral. The input text should be passed as a string to the function. If the input text is empty, the function should return 'neutral'.
  
  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
  
  assert analyze_sentiment("I love this product! This is the best thing ever.") == 'positive'
  assert analyze_sentiment("I hate this product. It's the worst.") == 'negative'
  assert analyze_sentiment("The product is okay.") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
output: def analyze_sentiment(text):
      if not text:
          return 'neutral'
      
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          elif sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          return 'negative'
      elif pos > neg:
          return 'positive'
      else:
          return 'neutral'