code: def test_zero_penalty():
      x, y, poly = multivariate_sample_data()
      alphas = [0, 0]
      gam_gs = GLMGam(y, smoother=poly, alpha=alphas)
      gam_gs_res = gam_gs.fit()
      y_est_gam = gam_gs_res.predict()
  
      glm = GLM(y, poly.basis).fit()
      y_est = glm.predict()
  
      assert_allclose(y_est, y_est_gam)
  
imports: import os
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  from scipy.linalg import block_diag
  import pytest
  from statsmodels.tools.linalg import matrix_sqrt
  from statsmodels.gam.smooth_basis import UnivariatePolynomialSmoother, PolynomialSmoother, BSplines, GenericSmoothers, UnivariateCubicSplines, CyclicCubicSplines
  from statsmodels.gam.generalized_additive_model import GLMGam, LogitGam, make_augmented_matrix, penalized_wls
  from statsmodels.gam.gam_cross_validation.gam_cross_validation import MultivariateGAMCV, MultivariateGAMCVPath, _split_train_test_smoothers
  from statsmodels.gam.gam_penalties import UnivariateGamPenalty, MultivariateGamPenalty
  from statsmodels.gam.gam_cross_validation.cross_validators import KFold
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.genmod.families.family import Gaussian
  from statsmodels.genmod.generalized_linear_model import lm
label_desc: Use image node_id 25 for calling a global function with example usage: test_zero_penalty() without return types