code: def temporal_difference(
      frames,
      use_grayscale=False,
      absolute=False,
  ):
      if use_grayscale:
          gray_channel = (
              0.299 * frames[2, :]
              + 0.587 * frames[1, :]
              + 0.114 * frames[0, :]
          )
          frames[0, :] = gray_channel
          frames[1, :] = gray_channel
          frames[2, :] = gray_channel
  
      out_images = torch.zeros_like(frames)
      t = frames.shape[1]
  
      dt = frames[:, 0 : t - 1, :, :] - frames[:, 1:t, :, :]
      if absolute:
          dt = dt.abs()
      out_images[:, 0 : t - 1, :, :] = dt
      if t <= 1:
          return out_images
      out_images[:, -1, :, :] = dt[:, -1, :, :]
      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 22 for calling a global function with example usage: temporal_difference(frames, use_grayscale, absolute) and returns: out_images, out_images