code: def pad(self, images: List[torch.Tensor]):
      max_size = tuple(
          max(s) for s in zip(*[img.shape for img in images])
      )
      image_sizes = [im.shape[-2:] for im in images]
      images = [
          F.pad(
              im,
              [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],
              value=self.pad_value,
          )
          for size, im in zip(image_sizes, images)
      ]
  
      return torch.stack(images), torch.tensor(image_sizes)
  
imports: import os
  import sys
  from dataclasses import dataclass
  from typing import List
  import numpy
  import omegaconf
  import torch
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.datasets.processors.processors import BaseProcessor
  from mmf.utils.download import get_image_from_url
  from PIL import Image
label_desc: Use image node_id 2 for calling the FRCNNPreprocess obj's underlying member method code with example usage: obj.pad(images) and returns: torch,  torch