input: from typing import List, Tuple
  
  def classify_texts(texts: List[Tuple[str, str]], new_text: str) -> str:
      """
      Classify text documents into two classes: positive and negative using a NaiveBayesClassifier.
      The texts are provided as a list of tuples, where each tuple contains a text and its label.
      The new_text is the text that needs to be classified.
  
      >>> classify_texts([("This is a great movie!", "positive"), ("This movie was terrible", "negative")], "This movie was ok")
      'positive'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier
  
      def label_words(words):
          return dict([(word, True) for word in words])
  
      train_data = []
      for text, label in texts:
          tokens = nltk.word_tokenize(text)
          train_data.append((label_words(tokens), label))
  
      classifier = NaiveBayesClassifier.train(train_data)
  
      new_tokens = nltk.word_tokenize(new_text)
      return classifier.classify(label_words(new_tokens))
output:     import nltk
      from nltk.classify import NaiveBayesClassifier
  
      def label_words(words):
          return dict([(word, True) for word in words])
  
      train_data = []
      for text, label in texts:
          tokens = nltk.word_tokenize(text)
          train_data.append((label_words(tokens), label))
  
      classifier = NaiveBayesClassifier.train(train_data)
  
      new_tokens = nltk.word_tokenize(new_text)
      return classifier.classify(label_words(new_tokens))