input: def analyze_sentiment(text):
      """ This function takes a text as input and analyzes its sentiment using a NaiveBayesClassifier.
      It categorizes the text into 'positive', 'negative', or 'neutral' sentiment based on the majority sentiment
      of the sentences within the text.
      >>> analyze_sentiment("I love this product. It is really great!")
      'positive'
      >>> analyze_sentiment("I hate this product. It is awful!")
      'negative'
      >>> analyze_sentiment("I am not sure about this product.")
      'neutral'
      """
output:     from textblob import TextBlob
      from textblob.sentiments import NaiveBayesAnalyzer
  
      blob = TextBlob(text, analyzer=NaiveBayesAnalyzer())
      sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}
  
      for sentence in blob.sentences:
          sentiment = sentence.sentiment.classification
          sentiment_counts[sentiment] += 1
  
      if sentiment_counts['positive'] > sentiment_counts['negative'] and sentiment_counts['positive'] > sentiment_counts['neutral']:
          return 'positive'
      elif sentiment_counts['negative'] > sentiment_counts['positive'] and sentiment_counts['negative'] > sentiment_counts['neutral']:
          return 'negative'
      else:
          return 'neutral'