code: def test_dict_learning_dtype_match(data_type, expected_type, method):
      # Verify output matrix dtype
      rng = np.random.RandomState(0)
      n_components = 8
      code, dictionary, _ = dict_learning(
          X.astype(data_type),
          n_components=n_components,
          alpha=1,
          random_state=rng,
          method=method,
      )
      assert code.dtype == expected_type
      assert dictionary.dtype == expected_type
  
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 42 for calling a global function with example usage: test_dict_learning_dtype_match(data_type, expected_type, method) without return types