input: 
  import nltk
  from nltk.classify import NaiveBayesClassifier 
    
  def classify_text_sentiment(text: str, classifier: NaiveBayesClassifier) -> str: 
      """ Classify the given text into 'positive' or 'negative' sentiment based on the provided NaiveBayesClassifier.
      >>> classifier.classify(label_words(nltk.word_tokenize("This movie was ok")))
      'positive'  # Example output may vary
      >>> classifier.classify(label_words(nltk.word_tokenize("This movie was terrible")))
      'negative'
      """
      def label_words(words): 
          return dict([(word, True) for word in words]) 
        
      tokens = nltk.word_tokenize(text) 
      labeled_words = label_words(tokens) 
      
      return classifier.classify(labeled_words)
  
output:     sentiment = classify_text_sentiment(text, classifier)
      return sentiment