code: def test_nli(self, device: str):
      nli_probabilities = 0.6 * torch.eye(3, device=device)
      nli = NaturalLanguageInference(0)
      nli(nli_probabilities)
  
      expected_scores = {
          "net_neutral": 0.6 / 3,
          "fraction_neutral": 1 / 3,
          "threshold_0.5": 1 / 3,
          "threshold_0.7": 0.0,
      }
      assert nli.get_metric(reset=True) == pytest.approx(
          expected_scores
      )
      assert all([v == 0.0 for k, v in nli.get_metric().items()])
  
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 2 for calling the NaturalLanguageInferenceTest obj's underlying member method code with example usage: obj.test_nli(device) without return types