code: def test_randaug(self):
      # Test default RandAugment.
      t, c, h, w = 8, 3, 200, 200
      test_time = 20
      video_tensor = torch.rand(t, c, h, w)
      video_rand_aug_fn = RandAugment()
      for _ in range(test_time):
          video_tensor_aug = video_rand_aug_fn(video_tensor)
          self.assertTrue(
              video_tensor.size() == video_tensor_aug.size()
          )
          self.assertTrue(video_tensor.dtype == video_tensor_aug.dtype)
          # Make sure the video is in range.
          self.assertTrue(video_tensor_aug.max().item() <= 1)
          self.assertTrue(video_tensor_aug.min().item() >= 0)
  
      # Test RandAugment with uniform sampling.
      t, c, h, w = 8, 3, 200, 200
      test_time = 20
      video_tensor = torch.rand(t, c, h, w)
      video_rand_aug_fn = RandAugment(sampling_type="uniform")
      for _ in range(test_time):
          video_tensor_aug = video_rand_aug_fn(video_tensor)
          self.assertTrue(
              video_tensor.size() == video_tensor_aug.size()
          )
          self.assertTrue(video_tensor.dtype == video_tensor_aug.dtype)
          # Make sure the video is in range.
          self.assertTrue(video_tensor_aug.max().item() <= 1)
          self.assertTrue(video_tensor_aug.min().item() >= 0)
  
      # Test if default fill color if found.
      # Test multiple times due to randomness.
      t, c, h, w = 8, 3, 200, 200
      test_time = 40
      video_tensor = torch.ones(t, c, h, w)
      video_rand_aug_fn = RandAugment(
          num_layers=1,
          prob=1,
          sampling_type="gaussian",
      )
      found_fill_color = 0
      for _ in range(test_time):
          video_tensor_aug = video_rand_aug_fn(video_tensor)
          if 0.5 in video_tensor_aug:
              found_fill_color += 1
      self.assertTrue(found_fill_color >= 1)
  
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 27 for calling the TestTransforms obj's underlying member method code with example usage: obj.test_randaug() without return types