code: def brightness_jitter(var, images):
      """
      Perfrom brightness jittering on the input images. The channels of images
      should be in order BGR.
      Args:
          var (float): jitter ratio for brightness.
          images (tensor): images to perform color jitter. Dimension is
              `num frames` x `channel` x `height` x `width`.
      Returns:
          images (tensor): the jittered images, the dimension is
              `num frames` x `channel` x `height` x `width`.
      """
      alpha = 1.0 + np.random.uniform(-var, var)
  
      img_bright = torch.zeros(images.shape)
      images = blend(images, img_bright, alpha)
      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 11 for calling a global function with example usage: brightness_jitter(var, images) and returns: images