code: def iris_data_with_estimator():
      def _make_estimator(**params):
          data = load_iris()
          x = data["data"]
          y = data["target"]
  
          meta = Stacking(
              LogisticRegression(**params),
              [
                  (
                      "tree",
                      DecisionTreeClassifier(
                          max_depth=1, random_state=0
                      ),
                  ),
                  (
                      "svm",
                      SVC(
                          probability=True, gamma="auto", random_state=0
                      ),
                  ),
              ],
          )
          return x, y, meta
  
      return _make_estimator
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from sklearn.base import BaseEstimator
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score, roc_auc_score
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from sksurv.ensemble import RandomSurvivalForest
  from sksurv.linear_model import CoxPHSurvivalAnalysis
  from sksurv.meta import MeanEstimator, Stacking
  from sksurv.svm import FastSurvivalSVM
  from sksurv.testing import assert_chf_properties, assert_cindex_almost_equal, assert_survival_function_properties
label_desc: Use image node_id 2 for calling a global function with example usage: iris_data_with_estimator() and returns: _make_estimator, x,  y,  meta