code: def test_unique_labels_mixed_types(setup):
      # Mix with binary or multiclass and multilabel
      mix_clf_format = product(
          EXAMPLES["multilabel-indicator"],
          EXAMPLES["multiclass"] + EXAMPLES["binary"],
      )
  
      for y_multilabel, y_multiclass in mix_clf_format:
          with pytest.raises(ValueError):
              unique_labels(y_multiclass, y_multilabel).execute()
          with pytest.raises(ValueError):
              unique_labels(y_multilabel, y_multiclass).execute()
  
      with pytest.raises(ValueError):
          unique_labels([[1, 2]], [["a", "d"]]).execute()
  
      with pytest.raises(ValueError):
          unique_labels(["1", 2]).execute()
  
      with pytest.raises(ValueError):
          unique_labels([["1", 2], [1, 3]]).execute()
  
      with pytest.raises(ValueError):
          unique_labels([["1", "2"], [2, 3]]).execute()
  
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 3 for calling a global function with example usage: test_unique_labels_mixed_types(setup) without return types