class_docstr: Resizes datasets to the specified size.
  Usually for upscaling datasets like CIFAR to Imagenet format
docstr: Creates an upsampler to make the supplied data match the pre-trained ViT format
  
  :param input_size: Size of the current input data
  :param final_size: Desired final size
docstr_tok: ["Creates","an","upsampler","to","make","the","supplied","data","match","the","pre-trained","ViT","format",":","param","input_size",":","Size","of","the","current","input","data",":","param","final_size",":","Desired","final","size"]
code: def __init__(self, input_size: int, final_size: int) -> None:
      """
      Creates an upsampler to make the supplied data match the pre-trained ViT format
  
      :param input_size: Size of the current input data
      :param final_size: Desired final size
      """
      super().__init__()
      self.upsample = torch.nn.Upsample(
          scale_factor=final_size / input_size
      )
  
imports: from typing import Optional, Union, Tuple
  import random
  import numpy
  import torch
  from art.estimators.certification.derandomized_smoothing.ablators.ablate import BaseAblator
label_desc: Use image node_id 1 to create a new UpSamplerPyTorch object from inherited base classes: torch.nn with example: obj = UpSamplerPyTorch(input_size, final_size)