code: def test_stacking_regression():
      from sklearn.datasets import load_diabetes
      from sklearn.ensemble import (
          RandomForestRegressor,
          StackingRegressor,
      )
      from sklearn.linear_model import RidgeCV
      from sklearn.model_selection import train_test_split
  
      X, y = load_diabetes(return_X_y=True)
      estimators = [
          (
              "gbm",
              xgb.sklearn.XGBRegressor(objective="reg:squarederror"),
          ),
          ("lr", RidgeCV()),
      ]
      reg = StackingRegressor(
          estimators=estimators,
          final_estimator=RandomForestRegressor(
              n_estimators=10, random_state=42
          ),
      )
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=42
      )
      reg.fit(X_train, y_train).score(X_test, y_test)
  
imports: import json
  import os
  import pickle
  import random
  import tempfile
  import warnings
  from typing import Callable, Optional
  import numpy
  import pytest
  from sklearn.utils.estimator_checks import parametrize_with_checks
  import xgboost
  from xgboost import testing
  from xgboost.testing.ranking import run_ranking_categorical, run_ranking_qid_df
  from xgboost.testing.shared import get_feature_weights, validate_data_initialization
  from xgboost.testing.updater import get_basescore
label_desc: Use image node_id 8 for calling a global function with example usage: test_stacking_regression() without return types