code: def test_unique_labels_non_specific():
      # Test unique_labels with a variety of collected examples
  
      # Smoke test for all supported format
      for format in ["binary", "multiclass", "multilabel-indicator"]:
          for y in EXAMPLES[format]:
              unique_labels(y)
  
      # We don't support those format at the moment
      for example in NON_ARRAY_LIKE_EXAMPLES:
          with pytest.raises(ValueError):
              unique_labels(example)
  
      for y_type in [
          "unknown",
          "continuous",
          "continuous-multioutput",
          "multiclass-multioutput",
      ]:
          for example in EXAMPLES[y_type]:
              with pytest.raises(ValueError):
                  unique_labels(example)
  
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 3 for calling a global function with example usage: test_unique_labels_non_specific() without return types