input: def classify_text(text: str, positive_text: str, negative_text: str) -> str:
      """ Classify the given text into two classes: 'positive' or 'negative'. This is done by
      training a NaiveBayesClassifier on provided positive and negative text samples.
      >>> classify_text("This movie was ok", "This is a great movie!", "This movie was terrible")
      'negative'
      """
      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) 
      
      return classifier.classify(label_words(nltk.word_tokenize(text)))
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(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(text)))