input: from typing import List, Dict
  
  def classify_text_document(documents: List[Dict[str, str]]) -> str:
      """ Classify a list of text documents into two classes: positive and negative.
      Each document is a dictionary where the key is the document text and the value is the expected classification.
      
      >>> classify_text_document([{'This is a great movie!': 'positive'}, {'This movie was terrible': 'negative'}])
      'Classified all documents correctly'
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier 
        
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      train_data = []
      for doc in documents:
          for text, label in doc.items():
              tokens = nltk.word_tokenize(text)
              train_data.append((label_words(tokens), label))
      
      classifier = NaiveBayesClassifier.train(train_data) 
      
      # classify and validate
      for doc in documents:
          for text, expected_label in doc.items():
              classified_label = classifier.classify(label_words(nltk.word_tokenize(text)))
              if classified_label != expected_label:
                  return 'Classification error'
      
      return 'Classified all documents correctly'
output:     nltk.download('punkt')