code: def color_jitter(
      images, img_brightness=0, img_contrast=0, img_saturation=0
  ):
      """
      Perfrom a color jittering on the input images. The channels of images
      should be in order BGR.
      Args:
          images (tensor): images to perform color jitter. Dimension is
              `num frames` x `channel` x `height` x `width`.
          img_brightness (float): jitter ratio for brightness.
          img_contrast (float): jitter ratio for contrast.
          img_saturation (float): jitter ratio for saturation.
      Returns:
          images (tensor): the jittered images, the dimension is
              `num frames` x `channel` x `height` x `width`.
      """
  
      jitter = []
      if img_brightness != 0:
          jitter.append("brightness")
      if img_contrast != 0:
          jitter.append("contrast")
      if img_saturation != 0:
          jitter.append("saturation")
  
      if len(jitter) > 0:
          order = np.random.permutation(np.arange(len(jitter)))
          for idx in range(0, len(jitter)):
              if jitter[order[idx]] == "brightness":
                  images = brightness_jitter(img_brightness, images)
              elif jitter[order[idx]] == "contrast":
                  images = contrast_jitter(img_contrast, images)
              elif jitter[order[idx]] == "saturation":
                  images = saturation_jitter(img_saturation, images)
      return 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 10 for calling a global function with example usage: color_jitter(images, img_brightness, img_contrast, img_saturation) and returns: images