code: def __call__(self, x):
      result = x.reshape(x.size(0), self.height, self.width, x.size(2))
  
      # Bring the channels to the first dimension,
      # like in CNNs.
      # [batch_size, H, W, C] -> [batch, C, H, W]
      result = result.permute(0, 3, 1, 2)
  
      return result
  
file_path: deep-learning-for-image-processing/pytorch_classification/grad_cam/main_swin.py
imports: import os
  import math
  import numpy
  import torch
  from PIL import Image
  import matplotlib.pyplot
  from torchvision import transforms
  from utils import GradCAM, show_cam_on_image, center_crop_img
  from swin_model import swin_base_patch4_window7_224
label_desc: Use image node_id 3 for calling the ResizeTransform obj's underlying member method code with example usage: obj.__call__(x) and returns: result