code: def test_dict_learning_reconstruction_parallel():
      # regression test that parallel reconstruction works with n_jobs>1
      n_components = 12
      dico = DictionaryLearning(
          n_components,
          transform_algorithm="omp",
          transform_alpha=0.001,
          random_state=0,
          n_jobs=4,
      )
      code = dico.fit(X).transform(X)
      assert_array_almost_equal(np.dot(code, dico.components_), X)
  
      dico.set_params(transform_algorithm="lasso_lars")
      code = dico.transform(X)
      assert_array_almost_equal(
          np.dot(code, dico.components_), X, 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 10 for calling a global function with example usage: test_dict_learning_reconstruction_parallel() without return types