code: def test_pmisq_masked_computation(self, device: str):
      ova_pmisq = AssociationWithoutGroundTruth(2, 2, "pmisq", "ova")
      pairwise_pmisq = AssociationWithoutGroundTruth(
          2, 2, "pmisq", "pairwise"
      )
      Y = torch.ones(3, 3, device=device).long()
      X = torch.eye(3, device=device).long()
      mask = torch.ones_like(Y).bool()
  
      expected_ova_pmisq_gaps = {
          0: [np.nan, round(math.log(2), 3)],
          1: [np.nan, round(math.log(0.5), 3)],
      }
      ova_pmisq(Y, X, mask)
      test_ova_pmisq_gaps = {
          k: [(round(e, 3) if not math.isnan(e) else np.nan) for e in v.tolist()]  # type: ignore
          for k, v in ova_pmisq.get_metric().items()
      }
      assert expected_ova_pmisq_gaps == test_ova_pmisq_gaps
  
      expected_pairwise_pmisq_gaps = {
          0: {0: [np.nan, 0.0], 1: [np.nan, round(math.log(2), 3)]},
          1: {0: [np.nan, round(math.log(0.5), 3)], 1: [np.nan, 0.0]},
      }
      pairwise_pmisq(Y, X, mask)
      test_pairwise_pmisq_gaps = {
          k1: {
              k2: [
                  (round(e, 3) if not math.isnan(e) else np.nan)
                  for e in v2.tolist()
              ]
              for k2, v2 in v1.items()
          }
          for k1, v1 in pairwise_pmisq.get_metric().items()
      }
      assert expected_pairwise_pmisq_gaps == test_pairwise_pmisq_gaps
  
imports: import pytest
  import torch
  import json
  import math
  import numpy
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.fairness.bias_metrics import WordEmbeddingAssociationTest, EmbeddingCoherenceTest, NaturalLanguageInference, AssociationWithoutGroundTruth
label_desc: Use image node_id 4 for calling the AssociationWithoutGroundTruthTest obj's underlying member method code with example usage: obj.test_pmisq_masked_computation(device) without return types