code: def test_fit_int_time(make_whas500, forest_cls):
      whas500 = make_whas500(to_numeric=True)
      y = whas500.y
      y_int = np.empty(
          y.shape[0],
          dtype=[(y.dtype.names[0], bool), (y.dtype.names[1], int)],
      )
      y_int[:] = y
  
      forest_f = forest_cls(oob_score=True, random_state=2).fit(
          whas500.x[50:], y[50:]
      )
      forest_i = forest_cls(oob_score=True, random_state=2).fit(
          whas500.x[50:], y_int[50:]
      )
  
      assert len(forest_f.estimators_) == len(forest_i.estimators_)
      assert forest_f.n_features_in_ == forest_i.n_features_in_
      assert forest_f.oob_score_ == forest_i.oob_score_
      assert_array_almost_equal(
          forest_f.unique_times_, forest_i.unique_times_
      )
  
      pred_f = forest_f.predict(whas500.x[:50])
      pred_i = forest_i.predict(whas500.x[:50])
  
      assert_array_almost_equal(pred_f, pred_i)
  
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 2 for calling a global function with example usage: test_fit_int_time(make_whas500, forest_cls) without return types