code: def _check_boxes(self, num_boxes, height, width, boxes):
      self.assertEqual(boxes.shape, (num_boxes, 4))
      self.assertTrue(
          boxes[:, [0, 2]].min() >= 0 and boxes[:, [0, 2]].max() < width
      )
      self.assertTrue(
          boxes[:, [1, 3]].min() >= 0
          and boxes[:, [1, 3]].max() < height
      )
  
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 26 for calling the TestTransforms obj's underlying member method code with example usage: obj._check_boxes(num_boxes, height, width, boxes) without return types