code: def test_multilabel_confusion_matrix_binary(setup):
      # Test multilabel confusion matrix - binary classification case
      y_true, y_pred, _ = make_prediction(binary=True)
      y_true = mt.tensor(y_true, chunk_size=40)
      y_pred = mt.tensor(y_pred, chunk_size=40)
  
      def run_test(y_true, y_pred):
          cm = multilabel_confusion_matrix(y_true, y_pred).fetch()
          assert_array_equal(
              cm, [[[17, 8], [3, 22]], [[22, 3], [8, 17]]]
          )
  
      run_test(y_true, y_pred)
      run_test(y_true.astype(str), y_pred.astype(str))
  
imports: import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_equal, assert_array_almost_equal
  from sklearn.utils._mocking import MockDataFrame
  from ....None import execute, fetch
  from ....None import tensor
  from ....lib.sparse import SparseNDArray
  from ..None import accuracy_score, log_loss
  from .._classification import _check_targets, multilabel_confusion_matrix, precision_recall_fscore_support, precision_score, recall_score, f1_score, fbeta_score
label_desc: Use image node_id 6 for calling a global function with example usage: test_multilabel_confusion_matrix_binary(setup) without return types