code: def inv_preprocess(imgs, num_images):
      """Inverse preprocessing of the batch of images.
         Add the mean vector and convert from BGR to RGB.
  
      Args:
        imgs: batch of input images.
        num_images: number of images to apply the inverse transformations on.
        img_mean: vector of mean colour values.
  
      Returns:
        The batch of the size num_images with the same spatial dimensions as the input.
      """
      rev_imgs = imgs[:num_images].clone().cpu().data
      rev_normalize = NormalizeInverse(
          mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
      )
      for i in range(num_images):
          rev_imgs[i] = rev_normalize(rev_imgs[i])
  
      return rev_imgs
  
file_path: insightface/parsing/dml_csr/utils/utils.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import torchvision
  import torch
  from PIL import Image
  from torch import nn
label_desc: Use image node_id 2 for calling a global function with example usage: inv_preprocess(imgs, num_images) and returns: rev_imgs