code: def test_is_multilabel_array_api_compliance(
      array_namespace, device, dtype_name
  ):
      xp = _array_api_for_tests(array_namespace, device)
  
      for group, group_examples in ARRAY_API_EXAMPLES.items():
          dense_exp = group == "multilabel-indicator"
          for example in group_examples:
              if np.asarray(example).dtype.kind == "f":
                  example = np.asarray(example, dtype=dtype_name)
              else:
                  example = np.asarray(example)
              example = xp.asarray(example, device=device)
  
              with config_context(array_api_dispatch=True):
                  assert dense_exp == is_multilabel(
                      example
                  ), f"is_multilabel({example!r}) should be {dense_exp}"
  
imports: from itertools import product
  import numpy
  import pytest
  from scipy.sparse import issparse
  from sklearn import config_context, datasets
  from sklearn.model_selection import ShuffleSplit
  from sklearn.svm import SVC
  from sklearn.utils._array_api import yield_namespace_device_dtype_combinations
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.estimator_checks import _NotAnArray
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
  from sklearn.utils.metaestimators import _safe_split
  from sklearn.utils.multiclass import _ovr_decision_function, check_classification_targets, class_distribution, is_multilabel, type_of_target, unique_labels
label_desc: Use image node_id 6 for calling a global function with example usage: test_is_multilabel_array_api_compliance(array_namespace, device, dtype_name) without return types