code: def _assert_connectivity_precision(connectivities, covs):
      """Estimated precision matrix: \
      - is positive definite, \
      - its product with the true covariance matrix \
        is close to the identity matrix."""
      for true_covariance_matrix, estimated_covariance_matrix in zip(
          covs, connectivities
      ):
          assert is_spd(estimated_covariance_matrix, decimal=7)
          assert_array_almost_equal(
              estimated_covariance_matrix.dot(true_covariance_matrix),
              np.eye(N_FEATURES),
          )
  
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 31 for calling a global function with example usage: _assert_connectivity_precision(connectivities, covs) without return types