code: def __call__(self, img):
      img = self.toTensor(img)
      img.sub_(0.5).div_(0.5)
      c, h, w = img.size()
      Pad_img = torch.FloatTensor(*self.max_size).fill_(0)
      Pad_img[:, :, :w] = img  # right pad
      if self.max_size[2] != w:  # add border Pad
          Pad_img[:, :, w:] = (
              img[:, :, w - 1]
              .unsqueeze(2)
              .expand(c, h, self.max_size[2] - w)
          )
  
      return Pad_img
  
imports: from PIL import Image
  import torch
  import torch.backends.cudnn
  import torch.utils.data
  import torch.nn.functional
  import torchvision.transforms
  import numpy
  from collections import OrderedDict
  import importlib
  from .utils import CTCLabelConverter
  import math
label_desc: Use image node_id 2 for calling the NormalizePAD obj's underlying member method code with example usage: obj.__call__(img) and returns: Pad_img