input: def classify_text_documents(text: str) -> str:
      """ Classify a given text document into 'positive' or 'negative' using a Naive Bayes Classifier.
      >>> classify_text_documents("This is a great movie!")
      'positive'
      >>> classify_text_documents("This movie was terrible")
      'negative'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Sample training data
      train_data = [
          "This is a great movie!",
          "This movie was terrible",
          "I loved this film",
          "I hated this film",
          "This was an amazing experience",
          "This was a waste of time"
      ]
      train_labels = ['positive', 'negative', 'positive', 'negative', 'positive', 'negative']
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(train_data, train_labels)
  
      # Predict the class of the input text
      return model.predict([text])[0]