code: def test_macro_averaged_mean_absolute_error(
      y_true, y_pred, expected_ma_mae
  ):
      ma_mae = macro_averaged_mean_absolute_error(y_true, y_pred)
      assert ma_mae == pytest.approx(expected_ma_mae, rel=R_TOL)
  
imports: from functools import partial
  import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.metrics import accuracy_score, average_precision_score, brier_score_loss, cohen_kappa_score, jaccard_score, precision_score, recall_score, roc_auc_score
  from sklearn.preprocessing import label_binarize
  from sklearn.utils._testing import assert_allclose, assert_array_equal, assert_no_warnings
  from sklearn.utils.validation import check_random_state
  from imblearn.metrics import classification_report_imbalanced, geometric_mean_score, macro_averaged_mean_absolute_error, make_index_balanced_accuracy, sensitivity_score, sensitivity_specificity_support, specificity_score
label_desc: Use image node_id 25 for calling a global function with example usage: test_macro_averaged_mean_absolute_error(y_true, y_pred, expected_ma_mae) without return types