code: def test_omp_gram_numerical_consistency():
      # verify numericaly consistency among np.float32 and np.float64
      coef_32 = orthogonal_mp_gram(
          G.astype(np.float32), Xy.astype(np.float32), n_nonzero_coefs=5
      )
      coef_64 = orthogonal_mp_gram(
          G.astype(np.float32), Xy.astype(np.float64), n_nonzero_coefs=5
      )
      assert_allclose(coef_32, coef_64)
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_sparse_coded_signal
  from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, orthogonal_mp, orthogonal_mp_gram
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings
label_desc: Use image node_id 20 for calling a global function with example usage: test_omp_gram_numerical_consistency() without return types