code: def test_distributed_sampler_and_uniform_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)
      clip_sampler = UniformClipSampler(video_clips, 3)
  
      distributed_sampler_rank0 = DistributedSampler(
          clip_sampler,
          num_replicas=2,
          rank=0,
          group_size=3,
      )
      indices = torch.tensor(list(iter(distributed_sampler_rank0)))
      assert len(distributed_sampler_rank0) == 6
      assert_equal(indices, torch.tensor([0, 2, 4, 10, 12, 14]))
  
      distributed_sampler_rank1 = DistributedSampler(
          clip_sampler,
          num_replicas=2,
          rank=1,
          group_size=3,
      )
      indices = torch.tensor(list(iter(distributed_sampler_rank1)))
      assert len(distributed_sampler_rank1) == 6
      assert_equal(indices, torch.tensor([5, 7, 9, 0, 2, 4]))
  
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 5 for calling the TestDatasetsSamplers obj's underlying member method code with example usage: obj.test_distributed_sampler_and_uniform_clip_sampler(tmpdir) without return types