class_docstr: Resizes datasets to the specified size.
  Usually for upscaling datasets like CIFAR to Imagenet format
docstr: Forward pass though the upsampler.
  
  :param x: Input data
  :return: The upsampled input data
docstr_tok: ["Forward","pass","though","the","upsampler",".",":","param","x",":","Input","data",":","return",":","The","upsampled","input","data"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Forward pass though the upsampler.
  
      :param x: Input data
      :return: The upsampled input data
      """
      return self.upsample(x)
  
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 2 for calling the UpSamplerPyTorch obj's underlying member method code with example usage: obj.forward(x) and returns: self