code: def test_rand_score_functional_is_symmetric(
      preds=_single_target_extrinsic1.preds,
      target=_single_target_extrinsic1.target,
  ):
      """Check that the metric functional is symmetric."""
      for p, t in zip(preds, target):
          assert torch.allclose(rand_score(p, t), rand_score(t, p))
  
file_path: torchmetrics/tests/unittests/clustering/test_rand_score.py
imports: import pytest
  import torch
  from sklearn.metrics import rand_score
  from torchmetrics.clustering.rand_score import RandScore
  from torchmetrics.functional.clustering.rand_score import rand_score
  from unittests.clustering.inputs import _float_inputs_extrinsic, _single_target_extrinsic1, _single_target_extrinsic2
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 2 for calling a global function with example usage: test_rand_score_functional_is_symmetric(preds, target) without return types