code: def test_metric_wrong():
      assert_raises(
          ValueError,
          (
              'metric must be either "r2", "accuracy", or a '
              "function with signature "
              "func(y_true, y_pred)."
          ),
          feature_importance_permutation,
          lambda x, y: (x, y),
          np.array([[1], [2], [3]]),
          np.array([1, 2, 3]),
          "some-metric",
      )
  
file_path: mlxtend/mlxtend/evaluate/tests/test_feature_importance.py
imports: import numpy
  import pandas
  from sklearn.datasets import make_classification, make_regression
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import mean_squared_error, r2_score
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.svm import SVC, SVR
  from mlxtend.evaluate import feature_importance_permutation
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 3 for calling a global function with example usage: test_metric_wrong() without return types