code: def test_ccp_alpha(self):
      est_full = self.assert_fit_and_predict(
          expected_cindex=None,
          n_estimators=10,
          max_leaf_nodes=20,
          random_state=1,
      )
  
      est_pruned = self.assert_fit_and_predict(
          expected_cindex=None,
          n_estimators=10,
          max_leaf_nodes=20,
          ccp_alpha=10.0,
          random_state=1,
      )
  
      tree = est_full.estimators_[0, 0].tree_
      subtree = est_pruned.estimators_[0, 0].tree_
      assert tree.node_count > subtree.node_count
      assert tree.max_depth > subtree.max_depth
  
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 10 for calling the TestGradientBoosting obj's underlying member method code with example usage: obj.test_ccp_alpha() without return types