code: def test_sparse_output_multilabel_binarizer_errors(csr_container):
      inp = iter([iter((2, 3)), iter((1,)), {1, 2}])
      mlb = MultiLabelBinarizer(sparse_output=False)
      mlb.fit(inp)
      with pytest.raises(ValueError):
          mlb.inverse_transform(
              csr_container(np.array([[0, 1, 1], [2, 0, 0], [1, 1, 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 14 for calling a global function with example usage: test_sparse_output_multilabel_binarizer_errors(csr_container) without return types