code: def test_dict_learning_positivity(
      transform_algorithm, positive_code, positive_dict
  ):
      n_components = 5
      dico = DictionaryLearning(
          n_components,
          transform_algorithm=transform_algorithm,
          random_state=0,
          positive_code=positive_code,
          positive_dict=positive_dict,
          fit_algorithm="cd",
      ).fit(X)
  
      code = dico.transform(X)
      if positive_dict:
          assert (dico.components_ >= 0).all()
      else:
          assert (dico.components_ < 0).any()
      if positive_code:
          assert (code >= 0).all()
      else:
          assert (code < 0).any()
  
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 6 for calling a global function with example usage: test_dict_learning_positivity(transform_algorithm, positive_code, positive_dict) without return types