code: def test_log_loss_with_sklearn(gt, probs):
      gt = np.array(gt, dtype=np.int64)
      probs = np.array(probs, dtype=np.float32)
      ag_loss = log_loss(gt, probs)
      sklearn_log_loss = sklearn.metrics.log_loss(gt, probs)
      # In AutoGluon, the metrics will always return score that is higher the better.
      # Thus, the true value should be the negation of the real log_loss
      np.testing.assert_allclose(ag_loss, -sklearn_log_loss)
  
      ag_loss_as_sklearn = log_loss.convert_score_to_original(ag_loss)
      np.testing.assert_allclose(ag_loss_as_sklearn, sklearn_log_loss)
  
imports: import numpy
  import pytest
  import sklearn
  from autogluon.core.metrics import confusion_matrix, log_loss, quadratic_kappa, roc_auc
  from autogluon.core.metrics.softclass_metrics import soft_log_loss
label_desc: Use image node_id 14 for calling a global function with example usage: test_log_loss_with_sklearn(gt, probs) without return types