code: def test_fit_predict(make_whas500, forest_cls, expected_c):
      whas500 = make_whas500(to_numeric=True)
  
      forest = forest_cls(random_state=2)
      forest.fit(whas500.x, whas500.y)
  
      assert len(forest.estimators_) == 100
  
      pred = forest.predict(whas500.x)
      assert np.isfinite(pred).all()
      assert np.all(pred >= 0)
  
      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 1 for calling a global function with example usage: test_fit_predict(make_whas500, forest_cls, expected_c) without return types