def METHOD_NAME(self) -> None:
    if not isinstance(self.eps, int) or self.eps < 0 or self.eps > 255:
        raise ValueError("The defense parameter must be between 0 and 255.")
    from art.estimators.classification.classifier import ClassifierMixin
    from art.estimators.estimator import NeuralNetworkMixin
    if hasattr(self, "pixel_cnn") and not (
        isinstance(self.pixel_cnn, ClassifierMixin) and isinstance(self.pixel_cnn, NeuralNetworkMixin)
    ):
        raise TypeError("PixelCNN model must be of type Classifier.")
    if np.array(self.clip_values[0] >= self.clip_values[1]).any():
        raise ValueError("Invalid `clip_values`: min >= max.")
    if self.clip_values[0] != 0:
        raise ValueError("`clip_values` min value must be 0.")
    if self.clip_values[1] != 1:
        raise ValueError("`clip_values` max value must be 1.")
    if self.batch_size <= 0:
        raise ValueError("The batch size `batch_size` has to be positive.")
    if not isinstance(self.verbose, bool):
        raise ValueError("The argument `verbose` has to be of type bool.")