code: def test_label_binarizer_sparse_errors(csr_container):
      # Fail on y_type
      err_msg = "foo format is not supported"
      with pytest.raises(ValueError, match=err_msg):
          _inverse_binarize_thresholding(
              y=csr_container([[1, 2], [2, 1]]),
              output_type="foo",
              classes=[1, 2],
              threshold=0,
          )
  
      # Fail on the number of classes
      err_msg = "The number of class is not equal to the number of dimension of y."
      with pytest.raises(ValueError, match=err_msg):
          _inverse_binarize_thresholding(
              y=csr_container([[1, 2], [2, 1]]),
              output_type="foo",
              classes=[1, 2, 3],
              threshold=0,
          )
  
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 7 for calling a global function with example usage: test_label_binarizer_sparse_errors(csr_container) without return types