code: def test_baseline(self):
      test_eval_result = self.create_eval_result()
      self.assertEqual(
          0.08,
          weight_utils.get_baseline_value(
              test_eval_result,
              "Overall",
              "post_export_metrics/negative_rate@0.10",
          ),
      )
      self.assertEqual(
          0.09,
          weight_utils.get_baseline_value(
              test_eval_result,
              (("gender", "female"),),
              "post_export_metrics/negative_rate@0.10",
          ),
      )
      # Test 'accuracy'.
      self.assertEqual(
          0.444,
          weight_utils.get_baseline_value(
              test_eval_result, "Overall", "accuracy"
          ),
      )
      # Test intersectional metrics.
      self.assertEqual(
          0.222,
          weight_utils.get_baseline_value(
              test_eval_result,
              (
                  ("gender", "female"),
                  ("sexual_orientation", "homosexual_gay_or_lesbian"),
              ),
              "accuracy",
          ),
      )
      with self.assertRaises(ValueError):
          # Test slice not found.
          weight_utils.get_baseline_value(
              test_eval_result, (("nonexistant", "slice"),), "accuracy"
          )
      with self.assertRaises(KeyError):
          # Test metric not found.
          weight_utils.get_baseline_value(
              test_eval_result,
              (("gender", "female"),),
              "nonexistent_metric",
          )
  
file_path: fairness-indicators/fairness_indicators/remediation/weight_utils_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from fairness_indicators.remediation import weight_utils
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling the WeightUtilsTest obj's underlying member method code with example usage: obj.test_baseline() without return types