code: def test_auc_gold_labels_behaviour(self, device: str):
      # Check that it works with different pos_label
      auc = Auc(positive_label=4)
  
      predictions = torch.randn(8, device=device)
      labels = torch.randint(
          3, 5, (8,), dtype=torch.long, device=device
      )
      # We make sure that the positive label is always present.
      labels[0] = 4
      auc(predictions, labels)
      computed_auc_value = auc.get_metric(reset=True)
  
      false_positive_rates, true_positive_rates, _ = metrics.roc_curve(
          labels.cpu().numpy(), predictions.cpu().numpy(), pos_label=4
      )
      real_auc_value = metrics.auc(
          false_positive_rates, true_positive_rates
      )
      assert_allclose(real_auc_value, computed_auc_value)
  
      # Check that it errs on getting more than 2 labels.
      with pytest.raises(ConfigurationError) as _:
          labels = torch.tensor(
              [3, 4, 5, 6, 7, 8, 9, 10], device=device
          )
          auc(predictions, labels)
  
file_path: allennlp/tests/training/metrics/auc_test.py
imports: import pytest
  import torch
  from sklearn import metrics
  from torch.testing import assert_allclose
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import Auc
label_desc: Use image node_id 2 for calling the AucTest obj's underlying member method code with example usage: obj.test_auc_gold_labels_behaviour(device) without return types