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
class_docstr_tok: ["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 _fit_classifier(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int,
      nb_epochs: int,
      **kwargs,
  ) -> None:
      x = x.astype(ART_NUMPY_DTYPE)
      return PyTorchClassifier.fit(
          self,
          x,
          y,
          batch_size=batch_size,
          nb_epochs=nb_epochs,
          **kwargs,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import warnings
  from tqdm.auto import trange
  import numpy
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 3 for calling the PyTorchRandomizedSmoothing obj's underlying member method code with example usage: obj._fit_classifier(x, y, batch_size, nb_epochs) and returns: PyTorchClassifier