class_docstr: Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
  in Cohen et al. (2019).
  
  | Paper link: https://arxiv.org/abs/1902.02918
docstr:  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.
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code: 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
          )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Union, TYPE_CHECKING, Tuple
  import warnings
  import numpy
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
  from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
  from art.estimators.classification import ClassifierMixin, ClassGradientsMixin
  from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
label_desc: Use image node_id 4 for calling the NumpyRandomizedSmoothing obj's underlying member method code with example usage: obj._fit_classifier(x, y, batch_size, nb_epochs) without return types