input: from typing import List, Tuple
  
  def classify_text(text_documents: List[Tuple[str, str]]) -> None:
      """ Classify text documents into two classes: positive and negative. The text
      documents are provided in the format of a list of tuples, where each tuple
      contains the text and the expected label ('positive' or 'negative'). The
      function trains a NaiveBayesClassifier using the provided text documents and 
      then classifies a sample text.
      >>> classify_text([("This is a great movie!", "positive"), ("This movie was terrible", "negative")])
      'positive'
      """
output:     from textblob.classifiers import NaiveBayesClassifier
  
      training_data = [(text, label) for text, label in text_documents]
      classifier = NaiveBayesClassifier(training_data)
  
      sample_text = "This movie was amazing!"
      classification = classifier.classify(sample_text)
      print(classification)