code: def test_label_binarize_binary():
      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
  from scipy.sparse import issparse
  from sklearn import datasets
  from sklearn.preprocessing._label import LabelBinarizer, LabelEncoder, MultiLabelBinarizer, _inverse_binarize_multiclass, _inverse_binarize_thresholding, label_binarize
  from sklearn.utils import _to_object_array
  from sklearn.utils._testing import assert_array_equal, ignore_warnings
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
  from sklearn.utils.multiclass import type_of_target
label_desc: Use image node_id 26 for calling a global function with example usage: test_label_binarize_binary() without return types