code: def test_tree_split_all_censored(veterans):
      X, y = veterans
      X = X.loc[:, "Karnofsky_score"].values[:, np.newaxis]
      y["Status"][X[:, 0] > 45.0] = False
  
      tree = SurvivalTree(max_depth=2, max_features=1)
      tree.fit(X, y)
  
      assert tree.tree_.capacity == 5
      assert_array_equal(
          tree.tree_.threshold,
          np.array(
              [
                  45.0,
                  25.0,
                  TREE_UNDEFINED,
                  TREE_UNDEFINED,
                  TREE_UNDEFINED,
              ]
          ),
      )
      expected_size = np.array([X.shape[0], 38, 8, 30, 99])
      assert_array_equal(tree.tree_.n_node_samples, expected_size)
  
imports: from itertools import product
  from queue import LifoQueue
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from scipy import sparse
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.tree._tree import TREE_UNDEFINED
  from sksurv.compare import compare_survival
  from sksurv.datasets import load_breast_cancer, load_veterans_lung_cancer
  from sksurv.nonparametric import kaplan_meier_estimator, nelson_aalen_estimator
  from sksurv.tree import SurvivalTree
  from sksurv.util import Surv
label_desc: Use image node_id 8 for calling a global function with example usage: test_tree_split_all_censored(veterans) without return types