code: def test_non_one_hot(self, mock_sklearn_metrics):
      mock_sklearn_metrics.roc_auc_score = Mock()
      metric = RocAucScore(one_hot_labels=False)
      metric.reset(
          {
              torchbearer.DEVICE: "cpu",
              torchbearer.DATA_TYPE: torch.float32,
          }
      )
      res = metric.process(
          {
              torchbearer.BATCH: 0,
              torchbearer.DEVICE: "cpu",
              torchbearer.DATA_TYPE: torch.float32,
              torchbearer.Y_TRUE: torch.LongTensor(
                  [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
              ),
              torchbearer.Y_PRED: torch.FloatTensor(
                  [[0.0, 0.0, 0.0], [1.1, 1.1, 1.1], [2.2, 2.2, 2.2]]
              ),
          }
      )
      self.assertTrue("roc_auc_score" in res)
      self.assertEqual(mock_sklearn_metrics.roc_auc_score.call_count, 1)
      self.assertTrue(
          np.array_equal(
              np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]),
              mock_sklearn_metrics.roc_auc_score.call_args_list[0][0][
                  0
              ],
          )
      )
      try:
          np.testing.assert_array_almost_equal(
              np.array(
                  [[0.0, 0.0, 0.0], [1.1, 1.1, 1.1], [2.2, 2.2, 2.2]]
              ),
              mock_sklearn_metrics.roc_auc_score.call_args_list[0][0][
                  1
              ],
          )
      except AssertionError:
          self.fail("y_pred not correctly passed to sklearn")
  
file_path: torchbearer/tests/metrics/test_roc_auc_score.py
imports: import unittest
  from mock import Mock, patch
  import torchbearer
  from torchbearer.metrics import RocAucScore, MetricList
  import torch
  import numpy
label_desc: Use image node_id 3 for calling the TestRocAucScore obj's underlying member method code with example usage: obj.test_non_one_hot(mock_sklearn_metrics) without return types