code: def test_normalized_mutual_info_score_functional_is_symmetric(
      average_method,
      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(
              normalized_mutual_info_score(p, t, average_method),
              normalized_mutual_info_score(t, p, average_method),
          )
  
file_path: torchmetrics/tests/unittests/clustering/test_normalized_mutual_info_score.py
imports: from functools import partial
  import pytest
  import torch
  from sklearn.metrics import normalized_mutual_info_score
  from torchmetrics.clustering import NormalizedMutualInfoScore
  from torchmetrics.functional.clustering import normalized_mutual_info_score
  from unittests import BATCH_SIZE, NUM_CLASSES
  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 3 for calling a global function with example usage: test_normalized_mutual_info_score_functional_is_symmetric(average_method, preds, target) without return types