code: def assert_unordered_list_compare_true(
      self,
      expected: List[Tuple[int, torch.Tensor]],
      actual: List[Tuple[int, torch.Tensor]],
  ):
      """
      Asserts True if all tuple values from expected found in actual and lengths are equal.
      """
      expected_str = str(
          [(label, clip.shape) for label, clip in expected]
      )
      actual = str([(label, clip.shape) for label, clip in actual])
      failure_str = (
          f"Expected set: {expected_str}\n actual set: {actual}"
      )
      self.assertTrue(unordered_list_compare, msg=failure_str)
  
imports: import collections
  import contextlib
  import itertools
  import math
  import multiprocessing
  import os
  import pathlib
  import tempfile
  import unittest
  import unittest.mock
  from fractions import Fraction
  from typing import List, Tuple
  from unittest.mock import Mock, patch
  import av
  import torch
  import torch.distributed
  from parameterized import parameterized
  from pytorchvideo.data import Hmdb51
  from pytorchvideo.data.clip_sampling import make_clip_sampler
  from pytorchvideo.data.labeled_video_dataset import labeled_video_dataset, LabeledVideoDataset
  from pytorchvideo.data.labeled_video_paths import LabeledVideoPaths
  from pytorchvideo.data.utils import MultiProcessSampler, thwc_to_cthw
  from torch.multiprocessing import Process
  from torch.utils.data import DataLoader, DistributedSampler, RandomSampler, SequentialSampler, TensorDataset
  from utils import create_dummy_video_frames, temp_encoded_video, temp_frame_video_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: assert_unordered_list_compare_true(expected, actual) without return types