code: def test_geometric_mean_properties_evaluate_convergence(p):
      n_matrices = 40
      n_features = 15
      # A warning is printed if tolerance is not reached
      spds = [
          random_spd(n_features, eig_min=1e-2, cond=1e6, random_state=0)
          for _ in range(int(p * n_matrices))
      ]
      spds.extend(
          random_spd(n_features, eig_min=1.0, cond=10.0, random_state=0)
          for _ in range(int(p * n_matrices), n_matrices)
      )
      max_iter = 30 if p < 1 else 60
  
      _geometric_mean(spds, max_iter=max_iter, tol=1e-5)
  
imports: import copy
  import warnings
  from math import cosh, exp, log, sinh, sqrt
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from pandas import DataFrame
  from scipy import linalg
  from sklearn.covariance import EmpiricalCovariance, LedoitWolf
  from nilearn._utils.extmath import is_spd
  from nilearn.connectome.connectivity_matrices import ConnectivityMeasure, _check_spd, _check_square, _form_symmetric, _geometric_mean, _map_eigenvalues, prec_to_partial, sym_matrix_to_vec, vec_to_sym_matrix
  from nilearn.tests.test_signal import generate_signals
label_desc: Use image node_id 19 for calling a global function with example usage: test_geometric_mean_properties_evaluate_convergence(p) without return types