code: def test_connectivity_measure_check_mean(kind, signals):
      conn_measure = ConnectivityMeasure(kind=kind)
      conn_measure.fit_transform(signals)
  
      assert (conn_measure.mean_).shape == (N_FEATURES, N_FEATURES)
  
      if kind != "tangent":
          assert_array_almost_equal(
              conn_measure.mean_,
              np.mean(conn_measure.transform(signals), axis=0),
          )
  
      # Check that the mean isn't modified in transform
      conn_measure = ConnectivityMeasure(kind="covariance")
      conn_measure.fit(signals[:1])
      mean = conn_measure.mean_
      conn_measure.transform(signals[1:])
  
      assert_array_equal(mean, conn_measure.mean_)
  
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 35 for calling a global function with example usage: test_connectivity_measure_check_mean(kind, signals) without return types