code: def test_label_binarize_binary(setup):
      y = [0, 1, 0]
      classes = [0, 1]
      pos_label = 2
      neg_label = -1
      expected = np.array([[2, -1], [-1, 2], [2, -1]])[:, 1].reshape(
          (-1, 1)
      )
  
      check_binarized_results(
          y, classes, pos_label, neg_label, expected
      )
  
      # Binary case where sparse_output = True will not result in a ValueError
      y = [0, 1, 0]
      classes = [0, 1]
      pos_label = 3
      neg_label = 0
      expected = np.array([[3, 0], [0, 3], [3, 0]])[:, 1].reshape(
          (-1, 1)
      )
  
      check_binarized_results(
          y, classes, pos_label, neg_label, expected
      )
  
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 7 for calling a global function with example usage: test_label_binarize_binary(setup) without return types