code: def __call__(self, image):
      if self.to_bgr255:
          image = image[[2, 1, 0]] * 255
      image = transforms.functional.normalize(
          image, mean=self.mean, std=self.std
      )
      if self.pad_size > 0:
          assert (
              self.pad_size >= image.shape[1]
              and self.pad_size >= image.shape[2]
          ), f"image size: {image.shape}"
          padded_image = image.new_zeros(
              3, self.pad_size, self.pad_size
          )
          padded_image[
              :, : image.shape[1], : image.shape[2]
          ] = image.clone()
  
          return padded_image
      return image
  
imports: import collections
  import math
  import random
  import warnings
  import torch
  from mmf.common.constants import INCEPTION_IMAGE_NORMALIZE
  from mmf.common.registry import registry
  from mmf.datasets.processors.processors import BaseProcessor
  from omegaconf import OmegaConf
  from torchvision import transforms
  from torchvision.transforms import Compose, Normalize, Resize, ToTensor
label_desc: Use image node_id 2 for calling the NormalizeBGR255 obj's underlying member method code with example usage: obj.__call__(image) and returns: image, padded_image