code: def test_dictionary_learning_dtype_match(
      data_type,
      expected_type,
      fit_algorithm,
      transform_algorithm,
  ):
      # Verify preserving dtype for fit and transform in dictionary learning class
      dict_learner = DictionaryLearning(
          n_components=8,
          fit_algorithm=fit_algorithm,
          transform_algorithm=transform_algorithm,
          random_state=0,
      )
      dict_learner.fit(X.astype(data_type))
      assert dict_learner.components_.dtype == expected_type
      assert (
          dict_learner.transform(X.astype(data_type)).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 40 for calling a global function with example usage: test_dictionary_learning_dtype_match(data_type, expected_type, fit_algorithm, transform_algorithm) without return types