code: def test_type_of_target(setup):
      raws = [
          np.array([[0, 1], [0, 0]]),  # multilabel
          np.random.randint(2, size=(5, 3, 3)),  # ndim > 2, unknown
          np.array([[]]),  # ndim == 2, shape[1] == 0, unknown
          np.array([[1, 2], [1, 2]]),
          np.array([1, 2, 3]),
          np.array([0.1, 0.2, 3]),
          np.array([[0.1, 0.2, 3]]),
          np.array([[1.0, 0.2]]),
          np.array([[1.0, 2.0, 3]]),
          np.array([[1, 2]]),
          np.array([1, 2]),
          np.array([["a"], ["b"]], dtype=object),
          [[1, 2]],
          [],  # empty list
      ]
  
      for raw in raws:
          assert type_of_target(
              raw
          ).to_numpy() == sklearn_type_of_target(raw)
  
      t = mt.tensor(raws[0], chunk_size=1)
      assert type_of_target(t).to_numpy() == sklearn_type_of_target(
          raws[0]
      )
  
      with pytest.raises(ValueError):
          type_of_target("sth")
  
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 5 for calling a global function with example usage: test_type_of_target(setup) without return types