code: def test_stacking_regressor_error(y, params, type_err, msg_err):
      with pytest.raises(type_err, match=msg_err):
          reg = StackingRegressor(**params, cv=3)
          reg.fit(
              scale(X_diabetes),
              y,
              sample_weight=np.ones(X_diabetes.shape[0]),
          )
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from scipy import sparse
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
  from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, StackingClassifier, StackingRegressor
  from sklearn.exceptions import ConvergenceWarning, NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, RidgeClassifier
  from sklearn.model_selection import KFold, StratifiedKFold, train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.preprocessing import scale
  from sklearn.svm import SVC, LinearSVC, LinearSVR
  from sklearn.utils._mocking import CheckingClassifier
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, ignore_warnings
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 10 for calling a global function with example usage: test_stacking_regressor_error(y, params, type_err, msg_err) without return types