code: def test_sparse_encode_error_default_sparsity():
      rng = np.random.RandomState(0)
      X = rng.randn(100, 64)
      D = rng.randn(2, 64)
      code = ignore_warnings(sparse_encode)(
          X, D, algorithm="omp", n_nonzero_coefs=None
      )
      assert code.shape == (100, 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 30 for calling a global function with example usage: test_sparse_encode_error_default_sparsity() without return types