code: def test_sparse_encode_unavailable_positivity(algo):
      n_components = 12
      rng = np.random.RandomState(0)
      V = rng.randn(n_components, n_features)  # random init
      V /= np.sum(V**2, axis=1)[:, np.newaxis]
      err_msg = (
          "Positive constraint not supported for '{}' coding method."
      )
      err_msg = err_msg.format(algo)
      with pytest.raises(ValueError, match=err_msg):
          sparse_encode(X, V, algorithm=algo, positive=True)
  
imports: import itertools
  import warnings
  from functools import partial
  import numpy
  import pytest
  import sklearn
  from sklearn.base import clone
  from sklearn.decomposition import DictionaryLearning, MiniBatchDictionaryLearning, SparseCoder, dict_learning, dict_learning_online, sparse_encode
  from sklearn.decomposition._dict_learning import _update_dict
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils import check_array
  from sklearn.utils._testing import TempMemmap, assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.estimator_checks import check_transformer_data_not_an_array, check_transformer_general, check_transformers_unfitted
  from sklearn.utils.parallel import Parallel
label_desc: Use image node_id 27 for calling a global function with example usage: test_sparse_encode_unavailable_positivity(algo) without return types