input: def classify_text(positiveText: str, negativeText: str) -> str:
      """Given two text documents, classify them into positive and negative classes. 
      The function uses Naive Bayes Classifier from the nltk library to train a model 
      on the provided text and then classifies a sample text.
      >>> classify_text("This is a great movie!", "This movie was terrible")
      '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(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)
      
      # classify 
      return classifier.classify(label_words(nltk.word_tokenize("This movie was ok")))
output:     import nltk
      from nltk.classify import NaiveBayesClassifier
      
      def label_words(words): 
          return dict([(word, True) for word in words])
  
      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)
      
      # classify 
      return classifier.classify(label_words(nltk.word_tokenize("This movie was ok")))