code: def glmgam_model():
      # Generalized Additive Model (GAM)
      from statsmodels.gam.tests.test_penalized import df_autos
  
      x_spline = df_autos[["weight", "hp"]]
      bs = sm.gam.BSplines(x_spline, df=[12, 10], degree=[3, 3])
      alpha = np.array([21833888.8, 6460.38479])
      gam_bs = sm.GLMGam.from_formula(
          "city_mpg ~ fuel + drive",
          data=df_autos,
          smoother=bs,
          alpha=alpha,
      )
      model = gam_bs.fit()
  
      return ModelWithResults(
          model=model, alg=gam_bs, inference_dataframe=df_autos
      )
  
imports: from collections import namedtuple
  import numpy
  import pandas
  import statsmodels.api
  from scipy.linalg import toeplitz
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.arima_process import arma_generate_sample
  from mlflow.models import ModelSignature
  from mlflow.types.schema import Schema, TensorSpec
label_desc: Use image node_id 13 for calling a global function with example usage: glmgam_model() and returns: ModelWithResults