code: def test_sufficiency_unmasked_computation(self, device: str):
      sufficiency = Sufficiency(2, 2)
  
      # Tests when C = 1 is not predicted
      C = torch.zeros(3, 3, device=device).long()
      Y = torch.eye(3, device=device).long()
      A = Y
  
      # P(Y | C = 0) = [0.667, 0.333]
      # P(Y | A = 0, C = 0) = [1.0, 0.0]
      # P(Y | A = 1, C = 0) = [0.0, 1.0]
      # KL(P(Y | A = 0, C = 0) || P(Y | C = 0)) = 0.4055
      # KL(P(Y | A = 1, C = 0) || P(Y | C = 0)) = 1.0986
  
      # P(Y | C = 1) = [0.0, 0.0]
      # P(Y | A = 0, C = 1) = [0.0, 0.0]
      # P(Y | A = 1, C = 1) = [0.0, 0.0]
      # KL(P(Y | A = 0, C = 1) || P(Y | C = 1)) = NaN
      # KL(P(Y | A = 1, C = 1) || P(Y | C = 1)) = NaN
  
      # KL divergence cannot be negative
      expected_kl_divs = {
          0: {0: 0.4055, 1: 1.0986},
          1: {0: np.nan, 1: np.nan},
      }
  
      sufficiency(C, Y, A)
      test_kl_divs = {
          k1: {
              k2: (v2.item() if not math.isnan(v2.item()) else np.nan)
              for k2, v2 in v1.items()
          }
          for k1, v1 in sufficiency.get_metric().items()
      }
      assert len(expected_kl_divs) == len(test_kl_divs)
      assert expected_kl_divs[0] == pytest.approx(
          test_kl_divs[0], abs=1e-3
      )
      assert expected_kl_divs[1] == test_kl_divs[1]
  
      sufficiency(C, Y, A)
      test_kl_divs = {
          k1: {
              k2: (v2.item() if not math.isnan(v2.item()) else np.nan)
              for k2, v2 in v1.items()
          }
          for k1, v1 in sufficiency.get_metric(reset=True).items()
      }
      assert len(expected_kl_divs) == len(test_kl_divs)
      assert expected_kl_divs[0] == pytest.approx(
          test_kl_divs[0], abs=1e-3
      )
      assert expected_kl_divs[1] == test_kl_divs[1]
  
      test_kl_divs = {
          k1: {
              k2: (v2.item() if not math.isnan(v2.item()) else np.nan)
              for k2, v2 in v1.items()
          }
          for k1, v1 in sufficiency.get_metric().items()
      }
      assert len(expected_kl_divs) == len(test_kl_divs)
      assert test_kl_divs == {
          0: {0: np.nan, 1: np.nan},
          1: {0: np.nan, 1: np.nan},
      }
  
imports: import pytest
  import torch
  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.fairness_metrics import Independence, Separation, Sufficiency
label_desc: Use image node_id 3 for calling the SufficiencyTest obj's underlying member method code with example usage: obj.test_sufficiency_unmasked_computation(device) without return types