code: def test_fit_no_bootstrap(make_whas500):
      whas500 = make_whas500(to_numeric=True)
  
      forest = RandomSurvivalForest(
          n_estimators=10, bootstrap=False, random_state=2
      )
      forest.fit(whas500.x, whas500.y)
  
      pred = forest.predict(whas500.x)
  
      expected_c = (0.931881994437717, 70030, 5119, 0, 14)
      assert_cindex_almost_equal(
          whas500.y["fstat"], whas500.y["lenfol"], pred, expected_c
      )
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pytest
  from scipy import sparse
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sksurv.datasets import load_breast_cancer
  from sksurv.ensemble import ExtraSurvivalTrees, RandomSurvivalForest
  from sksurv.preprocessing import OneHotEncoder
  from sksurv.testing import assert_chf_properties, assert_cindex_almost_equal, assert_survival_function_properties
  from sksurv.tree import SurvivalTree
label_desc: Use image node_id 8 for calling a global function with example usage: test_fit_no_bootstrap(make_whas500) without return types