code: def test_fit_kendall_correlation(make_whas500):
      whas500 = make_whas500(
          with_mean=False, with_std=False, to_numeric=True
      )
      meta = _create_survival_ensemble(correlation="kendall")
      assert len(meta) == 0
  
      meta.fit(whas500.x, whas500.y)
  
      p = meta.predict(whas500.x)
  
      assert_cindex_almost_equal(
          whas500.y["fstat"],
          whas500.y["lenfol"],
          p,
          (0.7663043, 57570, 17545, 34, 14),
      )
  
imports: import numpy
  from numpy.testing import assert_array_equal
  import pytest
  from sklearn.dummy import DummyRegressor
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import KFold, ParameterGrid
  from sksurv.ensemble import ComponentwiseGradientBoostingSurvivalAnalysis
  from sksurv.kernels import ClinicalKernelTransform
  from sksurv.linear_model import IPCRidge
  from sksurv.meta import EnsembleSelection, EnsembleSelectionRegressor
  from sksurv.metrics import concordance_index_censored
  from sksurv.svm import FastKernelSurvivalSVM, FastSurvivalSVM
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.tree import SurvivalTree
  from sksurv.util import check_array_survival
label_desc: Use image node_id 3 for calling the TestEnsembleSelectionSurvivalAnalysis obj's underlying member method code with example usage: obj.test_fit_kendall_correlation(make_whas500) without return types