input: def classify_documents(positive_text: str, negative_text: str, test_text: str) -> str:
      """ Classify a given text document into 'positive' or 'negative' using a NaiveBayesClassifier.
      The function tokenizes the input texts, labels them, and trains a classifier on them.
      The classifier is then used to classify a test text.
      >>> classify_documents("This is a great movie!", "This movie was terrible", "This movie was ok")
      'positive'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier
  
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_tokens = nltk.word_tokenize(positive_text) 
      negative_tokens = nltk.word_tokenize(negative_text)
  
      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) 
    
      # classify 
      return classifier.classify(label_words(nltk.word_tokenize(test_text)))
output:     test_tokens = nltk.word_tokenize(test_text)