code: def test_fit_predict_surv(make_whas500, forest_cls):
      whas500 = make_whas500(to_numeric=True)
  
      forest = forest_cls(n_estimators=10, random_state=2)
      forest.fit(whas500.x, whas500.y)
  
      assert len(forest.estimators_) == 10
  
      surv = forest.predict_survival_function(
          whas500.x, return_array=True
      )
      assert surv.shape == (500, forest.unique_times_.shape[0])
  
      assert_survival_function_properties(surv)
  
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 4 for calling a global function with example usage: test_fit_predict_surv(make_whas500, forest_cls) without return types