code: def augment_raw_frames(frames, time_diff_prob=0.0, gaussian_prob=0.0):
      frames = frames.float()
      if gaussian_prob > 0.0:
          blur_trans = tv.transforms.RandomApply(
              [GaussianBlurVideo()], p=gaussian_prob
          )
          frames = blur_trans(frames)
  
      time_diff_out = False
      if time_diff_prob > 0.0 and random.random() < time_diff_prob:
          # T H W C -> C T H W.
          frames = frames.permute(3, 0, 1, 2)
          frames = temporal_difference(
              frames, use_grayscale=True, absolute=False
          )
          # end_idx -= 1
          frames += 255.0
          frames /= 2.0
          # C T H W -> T H W C
          frames = frames.permute(1, 2, 3, 0)
          time_diff_out = True
  
      return frames, time_diff_out
  
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 24 for calling a global function with example usage: augment_raw_frames(frames, time_diff_prob, gaussian_prob) and returns: frames,  time_diff_out