input: Write a function to classify text documents into two classes: positive and negative using the Naive Bayes classifier from the nltk library. The function should take a list of tokenized words as input and return the predicted class.
  
  def classify_text(tokens):
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      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)
      
      return classifier.classify(label_words(tokens))
  
  assert classify_text(nltk.word_tokenize("This is a great movie!"))=='positive'
  assert classify_text(nltk.word_tokenize("This movie was terrible"))=='negative'
  assert classify_text(nltk.word_tokenize("This movie was ok"))=='positive'
output: def classify_text(tokens):
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      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)
      
      return classifier.classify(label_words(tokens))