code: def __call__(self, x):
      # remove cls token and reshape
      # [batch_size, num_tokens, token_dim]
      result = x[:, 1:, :].reshape(x.size(0), self.h, self.w, 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_vit.py
imports: import os
  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 vit_model import vit_base_patch16_224
label_desc: Use image node_id 2 for calling the ReshapeTransform obj's underlying member method code with example usage: obj.__call__(x) and returns: result