class_docstr: Interface class for the two De-randomized smoothing approaches supported by ART for pytorch.
  
  If a regular pytorch neural network is fed in then (De)Randomized Smoothing as introduced in Levine et al. (2020)
  is used.
  
  Otherwise, if a timm vision transfomer is fed in then Certified Patch Robustness via Smoothed Vision Transformers
  as introduced in Salman et al. (2021) is used.
docstr: Creates a vision transformer using PyTorchViT which controls the forward pass of the model
  
  :param variant: The name of the vision transformer to load
  :param pretrained: If to load pre-trained weights
  :return: A ViT with the required methods needed for ART
docstr_tok: ["Creates","a","vision","transformer","using","PyTorchViT","which","controls","the","forward","pass","of","the","model",":","param","variant",":","The","name","of","the","vision","transformer","to","load",":","param","pretrained",":","If","to","load","pre-trained","weights",":","return",":","A","ViT","with","the","required","methods","needed","for","ART"]
code: def create_vision_transformer(
      variant: str, pretrained: bool = False, **kwargs
  ) -> "PyTorchVisionTransformer":
      """
      Creates a vision transformer using PyTorchViT which controls the forward pass of the model
  
      :param variant: The name of the vision transformer to load
      :param pretrained: If to load pre-trained weights
      :return: A ViT with the required methods needed for ART
      """
  
      from timm.models._builder import build_model_with_cfg
      from timm.models.vision_transformer import checkpoint_filter_fn
      from art.estimators.certification.derandomized_smoothing.vision_transformers.pytorch import (
          PyTorchVisionTransformer,
      )
  
      return build_model_with_cfg(
          PyTorchVisionTransformer,
          variant,
          pretrained,
          pretrained_filter_fn=checkpoint_filter_fn,
          **kwargs,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import importlib
  import logging
  from typing import List, Optional, Tuple, Union, Any, TYPE_CHECKING
  import random
  import numpy
  from tqdm import tqdm
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.estimators.certification.derandomized_smoothing.derandomized import DeRandomizedSmoothingMixin
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 3 for calling the PyTorchDeRandomizedSmoothing obj's underlying member method code with example usage: obj.create_vision_transformer(variant, pretrained) and returns: build_model_with_cfg