code: def test_div_255(self):
      t, c, h, w = 8, 3, 200, 200
      video_tensor = torch.rand(t, c, h, w)
      output_tensor = div_255(video_tensor)
      expect_tensor = video_tensor / 255
  
      self.assertEqual(output_tensor.shape, video_tensor.shape)
      self.assertTrue(
          bool(torch.all(torch.eq(output_tensor, expect_tensor)))
      )
  
imports: import unittest
  from collections import Counter
  from itertools import permutations
  import numpy
  import torch
  from pytorchvideo.data.utils import thwc_to_cthw
  from pytorchvideo.transforms import ApplyTransformToKey, AugMix, create_video_transform, CutMix, MixUp, MixVideo, Normalize, OpSampler, Permute, RandAugment, RandomResizedCrop, RandomShortSideScale, ShortSideScale, UniformCropVideo, UniformTemporalSubsample
  from pytorchvideo.transforms.functional import clip_boxes_to_image, convert_to_one_hot, div_255, horizontal_flip_with_boxes, random_crop_with_boxes, random_short_side_scale_with_boxes, short_side_scale, short_side_scale_with_boxes, uniform_crop, uniform_crop_with_boxes, uniform_temporal_subsample, uniform_temporal_subsample_repeated
  from torchvision.transforms import Compose
  from torchvision.transforms._transforms_video import CenterCropVideo, NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo
  from utils import create_dummy_video_frames, create_random_bbox
label_desc: Use image node_id 32 for calling the TestTransforms obj's underlying member method code with example usage: obj.test_div_255() without return types