code: def test_prec_to_partial():
      precision = np.array(
          [
              [2.0, -1.0, 1.0],
              [-1.0, 2.0, -1.0],
              [1.0, -1.0, 1.0],
          ]
      )
      partial = np.array(
          [
              [1.0, 0.5, -sqrt(2.0) / 2.0],
              [0.5, 1.0, sqrt(2.0) / 2.0],
              [-sqrt(2.0) / 2.0, sqrt(2.0) / 2.0, 1.0],
          ]
      )
  
      assert_array_almost_equal(prec_to_partial(precision), partial)
  
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 27 for calling a global function with example usage: test_prec_to_partial() without return types