input: 
  from typing import List, Tuple
  import nltk
  from nltk.classify import NaiveBayesClassifier
  
  def classify_text(positive_text: str, negative_text: str, test_text: str) -> str:
      """Classify the given test text into 'positive' or 'negative' based on the training data provided.
      The training data consists of positive and negative text examples.
      >>> classify_text("This is a great movie!", "This movie was terrible", "This movie was ok")
      'negative'
      """
      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)
  
      return classifier.classify(label_words(nltk.word_tokenize(test_text)))
  
output:     test_tokens = nltk.word_tokenize(test_text)
      test_data = label_words(test_tokens)
      return classifier.classify(test_data)