code: def test_multivariate_gam_cv():
      # SMOKE test
      # no test is performed. It only checks that there is not any runtime error
  
      def cost(x1, x2):
          return np.linalg.norm(x1 - x2) / len(x1)
  
      cur_dir = os.path.dirname(os.path.abspath(__file__))
      file_path = os.path.join(
          cur_dir, "results", "prediction_from_mgcv.csv"
      )
      data_from_r = pd.read_csv(file_path)
      # dataset used to train the R model
      x = data_from_r.x.values
      y = data_from_r.y.values
  
      df = [10]
      degree = [5]
      bsplines = BSplines(x, degree=degree, df=df)
      # y_mgcv is obtained from R with the following code
      # g = gam(y~s(x, k = 10, bs = "cr"), data = data, scale = 80)
  
      alphas = [0.0251]
      alphas = [2]
      cv = KFold(3)
  
      gp = MultivariateGamPenalty(bsplines, alpha=alphas)  # noqa: F841
      gam_cv = MultivariateGAMCV(
          smoother=bsplines,
          alphas=alphas,
          gam=GLMGam,
          cost=cost,
          endog=y,
          exog=None,
          cv_iterator=cv,
      )
      gam_cv_res = gam_cv.fit()
  
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 16 for calling a global function with example usage: test_multivariate_gam_cv() and returns: unknown