input: Write a function to evaluate the accuracy of a decision tree model.
  assert evaluate_decision_tree_model(model, [[1, 2], [3, 4]], [0, 1]) == 1.0
  assert evaluate_decision_tree_model(model, [[5, 6], [7, 8]], [1, 1]) == 0.5
  assert evaluate_decision_tree_model(model, [[9, 10], [11, 12]], [1, 0]) == 0.0
output: def evaluate_decision_tree_model(model, X_test, y_test):
      predictions = model.predict(X_test)
      correct_predictions = sum(p == y for p, y in zip(predictions, y_test))
      accuracy = correct_predictions / len(y_test)
      return accuracy