input: 
  def text_document_classifier(document: str) -> str:
      """
      Classify a given text document into 'positive' or 'negative' categories.
      This function uses the NaiveBayesClassifier from the nltk library.
      >>> text_document_classifier("This is a great movie!")
      'positive'
      >>> text_document_classifier("This movie was terrible")
      'negative'
      >>> text_document_classifier("This movie was ok")
      'negative'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier 
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
  
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data) 
  
      return classifier.classify(label_words(nltk.word_tokenize(document))) 
  
output:     import nltk
      from nltk.classify import NaiveBayesClassifier 
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
  
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data) 
  
      return classifier.classify(label_words(nltk.word_tokenize(document)))