code: def test_is_multilabel(setup):
      raws = [
          [[1, 2]],
          [0, 1, 0, 1],
          # [[1], [0, 2], []], # deprecated in numpy v1.24
          np.array([[1, 0], [0, 0]]),
          np.array([[1], [0], [0]]),
          np.array([[1, 0, 0]]),
          np.array([[1.0, 0.0], [0.0, 0.0]]),
          sps.csr_matrix([[1, 0], [0, 1]]),
      ]
  
      for raw in raws:
          assert is_multilabel(raw).to_numpy() == sklearn_is_multilabel(
              raw
          )
  
      t = mt.tensor(raws[3], chunk_size=1)
      assert is_multilabel(t).to_numpy() == sklearn_is_multilabel(
          raws[3]
      )
  
file_path: mars/mars/learn/utils/tests/test_multiclass.py
imports: from itertools import product
  import numpy
  import pytest
  import scipy.sparse
  from scipy.sparse import csr_matrix
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils.estimator_checks import _NotAnArray
  from sklearn.utils.multiclass import is_multilabel, type_of_target
  from ....None import tensor
  from ..multiclass import is_multilabel, unique_labels, type_of_target
label_desc: Use image node_id 4 for calling a global function with example usage: test_is_multilabel(setup) without return types