code: def test_random_clip_sampler(self, tmpdir):
      video_list = get_list_of_videos(
          tmpdir, num_videos=3, sizes=[25, 25, 25]
      )
      video_clips = VideoClips(video_list, 5, 5)
      sampler = RandomClipSampler(video_clips, 3)
      assert len(sampler) == 3 * 3
      indices = torch.tensor(list(iter(sampler)))
      videos = torch.div(indices, 5, rounding_mode="floor")
      v_idxs, count = torch.unique(videos, return_counts=True)
      assert_equal(v_idxs, torch.tensor([0, 1, 2]))
      assert_equal(count, torch.tensor([3, 3, 3]))
  
file_path: vision/test/test_datasets_samplers.py
imports: import pytest
  import torch
  from common_utils import assert_equal, get_list_of_videos
  from torchvision import io
  from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
  from torchvision.datasets.video_utils import VideoClips
label_desc: Use image node_id 1 for calling the TestDatasetsSamplers obj's underlying member method code with example usage: obj.test_random_clip_sampler(tmpdir) without return types