def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
        """
         Fit the classifier on the training set `(x, y)`.

        :param x: Training data.
        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
                  (nb_samples,).
        :param batch_size: Batch size.
        :param nb_epochs: Number of epochs to use for training.
        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                       and providing it takes no effect.
        """

        g_a = GaussianAugmentation(sigma=self.scale, augmentation=False)
        for _ in range(nb_epochs):
            x_rs, _ = g_a(x)
            x_rs = x_rs.astype(ART_NUMPY_DTYPE)
            self.classifier.fit(x_rs, y, batch_size=batch_size, nb_epochs=1, **kwargs)

         Fit the classifier on the training set `(x, y)`.

        :param x: Training data.
        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
                  (nb_samples,).
        :param batch_size: Batch size.
        :param nb_epochs: Number of epochs to use for training.
        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                       and providing it takes no effect.
        
art/estimators/certification/randomized_smoothing/numpy.py