code: def test_label_encoder_negative_ints(setup):
      le = LabelEncoder()
      le.fit(mt.tensor([1, 1, 4, 5, -1, 0]))
      assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
      assert_array_equal(
          le.transform(mt.tensor([0, 1, 4, 4, 5, -1, -1])),
          [1, 2, 3, 3, 4, 0, 0],
      )
      assert_array_equal(
          le.inverse_transform(mt.tensor([1, 2, 3, 3, 4, 0, 0])),
          [0, 1, 4, 4, 5, -1, -1],
      )
      with pytest.raises(ValueError):
          le.transform(mt.tensor([0, 6]))
  
imports: import numpy
  import pytest
  import scipy.sparse
  from sklearn.preprocessing._label import _inverse_binarize_thresholding
  from sklearn.preprocessing._label import _inverse_binarize_multiclass
  from sklearn.utils._testing import assert_array_equal, ignore_warnings
  from sklearn.utils.multiclass import type_of_target
  from ....None import tensor
  from ..None import LabelBinarizer, label_binarize, LabelEncoder
label_desc: Use image node_id 13 for calling a global function with example usage: test_label_encoder_negative_ints(setup) without return types