code: def test_precision_refcall_f1_score_multilabel_unordered_labels(
      setup, average
  ):
      # test that labels need not be sorted in the multilabel case
      y_true = mt.array([[1, 1, 0, 0]])
      y_pred = mt.array([[0, 0, 1, 1]])
      p, r, f, s = precision_recall_fscore_support(
          y_true,
          y_pred,
          labels=[3, 0, 1, 2],
          warn_for=[],
          average=average,
      )
      p, r, f = fetch(execute(p, r, f))
      assert_array_equal(p, 0)
      assert_array_equal(r, 0)
      assert_array_equal(f, 0)
      if average is None:
          assert_array_equal(s, [0, 1, 1, 0])
  
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 12 for calling a global function with example usage: test_precision_refcall_f1_score_multilabel_unordered_labels(setup, average) without return types