input: from typing import List, Tuple
  
  def classify_text_documents(training_data: List[Tuple[str, str]], new_documents: List[str]) -> List[str]:
      """
      Classify given text documents into two classes: positive and negative using a NaiveBayesClassifier.
      
      >>> classify_text_documents([("This is a great movie!", 'positive'), ("This movie was terrible", 'negative')], ["This movie was ok"])
      ['negative']
      """
      
      import nltk
      from nltk.classify import NaiveBayesClassifier
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      training_samples = [(label_words(nltk.word_tokenize(text)), label) for text, label in training_data]
      classifier = NaiveBayesClassifier.train(training_samples)
      
      results = [classifier.classify(label_words(nltk.word_tokenize(doc))) for doc in new_documents]
      return results
output:     import nltk
      from nltk.classify import NaiveBayesClassifier
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      training_samples = [(label_words(nltk.word_tokenize(text)), label) for text, label in training_data]
      classifier = NaiveBayesClassifier.train(training_samples)
      
      results = [classifier.classify(label_words(nltk.word_tokenize(doc))) for doc in new_documents]
      return results