code: def test_unique_labels_mixed_types():
      # 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)
          with pytest.raises(ValueError):
              unique_labels(y_multilabel, y_multiclass)
  
      with pytest.raises(ValueError):
          unique_labels([[1, 2]], [["a", "d"]])
  
      with pytest.raises(ValueError):
          unique_labels(["1", 2])
  
      with pytest.raises(ValueError):
          unique_labels([["1", 2], [1, 3]])
  
      with pytest.raises(ValueError):
          unique_labels([["1", "2"], [2, 3]])
  
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 4 for calling a global function with example usage: test_unique_labels_mixed_types() without return types