code: def test_unsupported_regressor():
      # including regressor that does not support
      # sample_weight should raise error
      lr = LinearRegression()
      svr_lin = SVR(kernel="linear", gamma="auto")
      ridge = Ridge(random_state=1)
      knn = KNeighborsRegressor()
      svr_rbf = SVR(kernel="rbf", gamma="auto")
      stack = StackingCVRegressor(
          regressors=[svr_lin, lr, ridge, knn], meta_regressor=svr_rbf
      )
      with pytest.raises(TypeError):
          stack.fit(X1, y, sample_weight=w).predict(X1)
  
imports: import random
  import numpy
  import pytest
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn.base import clone
  from sklearn.linear_model import Lasso, LinearRegression, Ridge
  from sklearn.model_selection import GridSearchCV, KFold, train_test_split
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import SVR
  from mlxtend.externals.estimator_checks import NotFittedError
  from mlxtend.regressor import StackingCVRegressor
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 17 for calling a global function with example usage: test_unsupported_regressor() without return types