code: def test_wrong_metric_name(self):
      X = np.random.uniform(size=(30, 2))
      y = np.random.randint(0, 2, size=(30,))
      S = pd.DataFrame({"sensitive": ["A", "B"] * 15})
  
      with self.assertRaises(ValueError) as context:
          automl = AutoML(
              results_path=self.automl_dir,
              model_time_limit=10,
              algorithms=["Xgboost"],
              privileged_groups=[{"sensitive": "A"}],
              underprivileged_groups=[{"sensitive": "B"}],
              fairness_metric="wrong_metric_name",
              fairness_threshold=0.2,
              explain_level=0,
              train_ensemble=False,
              stack_models=False,
              validation_strategy={"validation_type": "split"},
              start_random_models=1,
          )
          automl.fit(X, y, sensitive_features=S)
      self.assertTrue("is not allowed" in str(context.exception))
  
file_path: mljar-supervised/tests/tests_fairness/test_binary_classification.py
imports: import os
  import shutil
  import unittest
  import numpy
  import pandas
  from supervised.utils.utils import dump_data
  from supervised.validation.validator_kfold import KFoldValidator
  import shutil
  from supervised import AutoML
label_desc: Use image node_id 4 for calling the FairnessInBinaryClassificationTest obj's underlying member method code with example usage: obj.test_wrong_metric_name() without return types