code: def color_normalization(images, mean, stddev):
      """
      Perform color nomration on the given images.
      Args:
          images (tensor): images to perform color normalization. Dimension is
              `num frames` x `channel` x `height` x `width`.
          mean (list): mean values for normalization.
          stddev (list): standard deviations for normalization.
  
      Returns:
          out_images (tensor): the noramlized images, the dimension is
              `num frames` x `channel` x `height` x `width`.
      """
      if len(images.shape) == 3:
          assert (
              len(mean) == images.shape[0]
          ), "channel mean not computed properly"
          assert (
              len(stddev) == images.shape[0]
          ), "channel stddev not computed properly"
      elif len(images.shape) == 4:
          assert (
              len(mean) == images.shape[1]
          ), "channel mean not computed properly"
          assert (
              len(stddev) == images.shape[1]
          ), "channel stddev not computed properly"
      else:
          raise NotImplementedError(
              f"Unsupported dimension {len(images.shape)}"
          )
  
      out_images = torch.zeros_like(images)
      for idx in range(len(mean)):
          # C H W
          if len(images.shape) == 3:
              out_images[idx] = (images[idx] - mean[idx]) / stddev[idx]
          elif len(images.shape) == 4:
              out_images[:, idx] = (
                  images[:, idx] - mean[idx]
              ) / stddev[idx]
          else:
              raise NotImplementedError(
                  f"Unsupported dimension {len(images.shape)}"
              )
      return out_images
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 15 for calling a global function with example usage: color_normalization(images, mean, stddev) and returns: out_images