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
code: def compute_loss(
      self, x: np.ndarray, y: np.ndarray, **kwargs
  ) -> np.ndarray:
      return self.classifier.compute_loss(x=x, y=y, **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 7 for calling the NumpyRandomizedSmoothing obj's underlying member method code with example usage: obj.compute_loss(x, y) and returns: self