code: def test_dict_learning_lassocd_readonly_data():
      n_components = 12
      with TempMemmap(X) as X_read_only:
          dico = DictionaryLearning(
              n_components,
              transform_algorithm="lasso_cd",
              transform_alpha=0.001,
              random_state=0,
              n_jobs=4,
          )
          with ignore_warnings(category=ConvergenceWarning):
              code = dico.fit(X_read_only).transform(X_read_only)
          assert_array_almost_equal(
              np.dot(code, dico.components_), X_read_only, decimal=2
          )
  
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 11 for calling a global function with example usage: test_dict_learning_lassocd_readonly_data() without return types