code: def test_covariance_scm_real(rndstate, assume_centered):
      """Test equivalence between pyriemann and sklearn estimator on real data"""
      n_matrices, n_channels, n_times = 3, 4, 50
      x = rndstate.randn(n_matrices, n_channels, n_times)
  
      cov = covariances(
          x, estimator="scm", assume_centered=assume_centered
      )
      cov_sklearn = np.asarray(
          [
              empirical_covariance(
                  x_.T, assume_centered=assume_centered
              )
              for x_ in x
          ]
      )
      assert_array_almost_equal(cov, cov_sklearn, 10)
  
imports: from numpy.testing import assert_array_almost_equal
  import numpy
  from scipy.linalg import block_diag
  from scipy.signal import welch, csd, coherence
  from sklearn.covariance import empirical_covariance
  import pytest
  from pyriemann.utils.covariance import covariances, covariances_EP, covariances_X, eegtocov, cross_spectrum, cospectrum, coherence, normalize, get_nondiag_weight, block_covariances, _complex_estimator
  from pyriemann.utils.test import is_real, is_hermitian, is_sym_pos_def, is_herm_pos_def
label_desc: Use image node_id 2 for calling a global function with example usage: test_covariance_scm_real(rndstate, assume_centered) without return types