code: def breast_cancer():
      X, y = load_breast_cancer()
      X["er"] = X.loc[:, "er"].replace({"negative": 0, "positive": 1})
      X["grade"] = X.loc[:, "grade"].replace(
          {
              "intermediate": 0,
              "poorly differentiated": 1,
              "unkown": 2,
              "well differentiated": 3,
          }
      )
      return X, y
  
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 2 for calling a global function with example usage: breast_cancer() and returns: X,  y