code: def test_sparse_coder_dtype_match(data_type, transform_algorithm):
      # Verify preserving dtype for transform in sparse coder
      n_components = 6
      rng = np.random.RandomState(0)
      dictionary = rng.randn(n_components, n_features)
      coder = SparseCoder(
          dictionary.astype(data_type),
          transform_algorithm=transform_algorithm,
      )
      code = coder.transform(X.astype(data_type))
      assert code.dtype == data_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 39 for calling a global function with example usage: test_sparse_coder_dtype_match(data_type, transform_algorithm) without return types