code: def test_connectivity_measure_specific_for_each_kind(
      kind, cov_estimator, signals_and_covariances
  ):
      signals, covs = signals_and_covariances
  
      conn_measure = ConnectivityMeasure(
          kind=kind, cov_estimator=cov_estimator
      )
      connectivities = conn_measure.fit_transform(signals)
  
      if kind == "tangent":
          _assert_connectivity_tangent(
              connectivities, conn_measure, covs
          )
      elif kind == "precision":
          _assert_connectivity_precision(connectivities, covs)
      elif kind == "correlation":
          _assert_connectivity_correlation(
              connectivities, cov_estimator, covs
          )
      elif kind == "partial correlation":
          _assert_connectivity_partial_correlation(connectivities, covs)
  
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 34 for calling a global function with example usage: test_connectivity_measure_specific_for_each_kind(kind, cov_estimator, signals_and_covariances) without return types