code: def test_classification_with_custom_names(self):
      roc_names = "roc"
      self._test_classification(roc_auc_score, True, names=roc_names)
      self._test_classification(roc_auc_score, False, names=roc_names)
  
      ap_names = "ap"
      self._test_classification(
          average_precision_score, True, names=ap_names
      )
      self._test_classification(
          average_precision_score, False, names=ap_names
      )
  
      two_names = ["roc", "ap"]
      two_skl_metrics = [roc_auc_score, average_precision_score]
      self._test_classification(two_skl_metrics, True, names=two_names)
      self._test_classification(two_skl_metrics, False, names=two_names)
  
file_path: poutyne/tests/framework/metrics/test_sklearn_metrics.py
imports: from itertools import repeat
  from unittest import TestCase, skipIf
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from poutyne import SKLearnMetrics, TensorDataset
label_desc: Use image node_id 5 for calling the SKLearnMetricsTest obj's underlying member method code with example usage: obj.test_classification_with_custom_names() without return types