def __init__(
        self,
        model,
        nb_classes: int,
        input_shape: tuple[int, ...],
        loss_object: "tf.Tensor" | None = None,
        optimizer: "tf.keras.optimizers.Optimizer" | None = None,
        train_step: Callable | None = None,
        channels_first: bool = False,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
        sample_size: int = 32,
        scale: float = 0.1,
        alpha: float = 0.001,
        epsilon: float = 1.0,
        num_noise_vec: int = 1,
        num_steps: int = 10,
        warmup: int = 1,
    ) -> None:
        """
        Create a MACER classifier.

        :param model: a python functions or callable class defining the model and providing it prediction as output.
        :type model: `function` or `callable class`
        :param nb_classes: the number of classes in the classification task.
        :param input_shape: Shape of one input for the classifier, e.g. for MNIST input_shape=(28, 28, 1).
        :param loss_object: The loss function for which to compute gradients. This parameter is applied for training
               the model and computing gradients of the loss w.r.t. the input.
        :param optimizer: The optimizer used to train the classifier.
        :param train_step: A function that applies a gradient update to the trainable variables with signature
               `train_step(model, images, labels)`. This will override the default training loop that uses the
               provided `loss_object` and `optimizer` parameters. It is recommended to use the `@tf.function`
               decorator, if possible, for efficient training.
        :param channels_first: Set channels first or last.
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param sample_size: Number of samples for smoothing.
        :param scale: Standard deviation of Gaussian noise added.
        :param alpha: The failure probability of smoothing.
        :param epsilon: The maximum perturbation that can be induced.
        :param num_noise_vec: The number of noise vectors.
        :param num_steps: The number of attack updates.
        :param warmup: The warm-up strategy that is gradually increased up to the original value.
        """
        super().__init__(
            model=model,
            nb_classes=nb_classes,
            input_shape=input_shape,
            loss_object=loss_object,
            optimizer=optimizer,
            train_step=train_step,
            channels_first=channels_first,
            clip_values=clip_values,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            sample_size=sample_size,
            scale=scale,
            alpha=alpha,
        )
        self.epsilon = epsilon
        self.num_noise_vec = num_noise_vec
        self.num_steps = num_steps
        self.warmup = warmup

        classifier = TensorFlowV2Classifier(
            model=model,
            nb_classes=nb_classes,
            input_shape=input_shape,
            loss_object=loss_object,
            optimizer=optimizer,
            train_step=train_step,
            channels_first=channels_first,
            clip_values=clip_values,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
        )
        self.attack = ProjectedGradientDescent(classifier, eps=self.epsilon, max_iter=1, verbose=False)

        Create a MACER classifier.

        :param model: a python functions or callable class defining the model and providing it prediction as output.
        :type model: `function` or `callable class`
        :param nb_classes: the number of classes in the classification task.
        :param input_shape: Shape of one input for the classifier, e.g. for MNIST input_shape=(28, 28, 1).
        :param loss_object: The loss function for which to compute gradients. This parameter is applied for training
               the model and computing gradients of the loss w.r.t. the input.
        :param optimizer: The optimizer used to train the classifier.
        :param train_step: A function that applies a gradient update to the trainable variables with signature
               `train_step(model, images, labels)`. This will override the default training loop that uses the
               provided `loss_object` and `optimizer` parameters. It is recommended to use the `@tf.function`
               decorator, if possible, for efficient training.
        :param channels_first: Set channels first or last.
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param sample_size: Number of samples for smoothing.
        :param scale: Standard deviation of Gaussian noise added.
        :param alpha: The failure probability of smoothing.
        :param epsilon: The maximum perturbation that can be induced.
        :param num_noise_vec: The number of noise vectors.
        :param num_steps: The number of attack updates.
        :param warmup: The warm-up strategy that is gradually increased up to the original value.
        
art/estimators/certification/randomized_smoothing/smooth_adv/tensorflow.py