code: def test_mean_riemann(kind, init, get_mats_params):
      """Test the Riemannian mean with same eigen vectors"""
      n_matrices, n_channels = 10, 3
      mats, eigvals, eigvecs = get_mats_params(
          n_matrices, n_channels, kind
      )
      if init:
          C = mean_riemann(mats, init=mats[0])
      else:
          C = mean_riemann(mats)
      eigval = np.exp(np.mean(np.log(eigvals), axis=0))
      Ctrue = eigvecs @ np.diag(eigval) @ eigvecs.conj().T
      assert C == approx(Ctrue)
  
imports: import numpy
  from scipy.stats import gmean
  import pytest
  from pytest import approx
  from pyriemann.utils.geodesic import geodesic_riemann
  from pyriemann.utils.mean import mean_covariance, mean_ale, mean_alm, mean_euclid, mean_harmonic, mean_identity, mean_kullback_sym, mean_logdet, mean_logeuclid, mean_power, mean_riemann, mean_wasserstein, maskedmean_riemann, nanmean_riemann
label_desc: Use image node_id 13 for calling a global function with example usage: test_mean_riemann(kind, init, get_mats_params) without return types