input: from typing import List
  
  def classify_text(texts: List[str], classifier) -> List[str]:
      """
      Classify a list of text documents into two classes: positive and negative using 
      a pre-trained classifier.
      
      >>> 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)
      >>> classify_text(["This movie was ok", "This movie was amazing"], classifier)
      ['negative', 'positive']
      """
      return [classifier.classify(label_words(nltk.word_tokenize(text))) for text in texts]
output:     return [classifier.classify(label_words(nltk.word_tokenize(text))) for text in texts]