input: 
  def analyze_sentiment(text: str) -> str:
      """ Analyze the sentiment of a given text by categorizing it into positive, negative, and neutral sentiments.
      >>> analyze_sentiment("I had a great day today")
      'positive'
      >>> analyze_sentiment("This is a really bad situation")
      'negative'
      >>> analyze_sentiment("Just another regular day")
      'neutral'
      """
      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:     from nltk.tokenize import sent_tokenize
      from nltk.classify import NaiveBayesClassifier