code: def feature_size(s):
      s = math.ceil(s / 4)  # PatchEmbed
      s = math.ceil(s / 2)  # PatchMerging1
      s = math.ceil(s / 2)  # PatchMerging2
      s = math.ceil(s / 2)  # PatchMerging3
      return s
  
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 2 for calling the ResizeTransform obj's underlying member method code with example usage: obj.feature_size(s) and returns: s