code: def test_fit(self):
      model = self.assert_fit_and_predict(
          expected_cindex=(0.86272605091218779, 64826, 10309, 14, 14),
          n_estimators=100,
          max_depth=3,
          min_samples_split=10,
      )
  
      assert model.max_features_ == 14
      assert not hasattr(model, "oob_improvement_")
  
      assert (100,) == model.train_score_.shape
  
imports: from contextlib import nullcontext
  from os.path import dirname, join
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from sklearn.metrics import mean_squared_error
  from sksurv.column import categorical_to_numeric, standardize
  from sksurv.datasets import load_whas500
  from sksurv.ensemble import ComponentwiseGradientBoostingSurvivalAnalysis, GradientBoostingSurvivalAnalysis
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.util import Surv
label_desc: Use image node_id 3 for calling the TestGradientBoosting obj's underlying member method code with example usage: obj.test_fit() without return types