code: def test_pipeline_predict(breast_cancer, func):
      X_num, y = breast_cancer
      X_num = X_num.loc[:, ["er", "grade"]].values
  
      tree = SurvivalTree().fit(X_num[10:], y[10:])
  
      X_str, _ = load_breast_cancer()
      X_str = X_str.loc[:, ["er", "grade"]].values
  
      pipe = make_pipeline(OrdinalEncoder(), SurvivalTree())
      pipe.fit(X_str[10:], y[10:])
  
      tree_pred = getattr(tree, func)(X_num[:10], return_array=True)
      pipe_pred = getattr(pipe, func)(X_str[:10], return_array=True)
  
      assert_array_almost_equal(tree_pred, pipe_pred)
  
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 15 for calling a global function with example usage: test_pipeline_predict(breast_cancer, func) without return types