code: def test_stacking_regressor(self):
      X, y = load_diabetes(return_X_y=True)
      estimators = [
          ("lr", RidgeCV()),
          ("svr", LinearSVR(random_state=42)),
      ]
      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)
  
      hb_model = hummingbird.ml.convert(reg, "torch")
  
      np.testing.assert_allclose(
          reg.predict(X_test),
          hb_model.predict(X_test),
          rtol=1e-06,
          atol=1e-06,
      )
  
imports: import unittest
  import numpy
  from sklearn import datasets
  from sklearn.compose import ColumnTransformer
  from sklearn.datasets import load_iris, load_diabetes
  from sklearn.svm import LinearSVC, LinearSVR
  from sklearn.datasets import make_regression
  from sklearn.decomposition import PCA
  from sklearn.linear_model import LogisticRegression, RidgeCV
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler
  import hummingbird.ml
  from hummingbird.ml._utils import pandas_installed, onnx_runtime_installed
  from hummingbird.ml import constants
  from onnxconverter_common.data_types import FloatTensorType, Int64TensorType, StringTensorType
label_desc: Use image node_id 23 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_stacking_regressor() without return types