code: def gee_model():
      # Example taken from
      # https://www.statsmodels.org/devel/examples/notebooks/generated/gee_nested_simulation.html
      np.random.seed(9876789)
      p = 5
      groups_var = 1
      level1_var = 2
      level2_var = 3
      resid_var = 4
      n_groups = 100
      group_size = 20
      level1_size = 10
      level2_size = 5
      n = n_groups * group_size * level1_size * level2_size
      xmat = np.random.normal(size=(n, p))
  
      # Construct labels showing which group each observation belongs to at each level.
      groups_ix = np.kron(
          np.arange(n // group_size), np.ones(group_size)
      ).astype(int)
      level1_ix = np.kron(
          np.arange(n // level1_size), np.ones(level1_size)
      ).astype(int)
      level2_ix = np.kron(
          np.arange(n // level2_size), np.ones(level2_size)
      ).astype(int)
  
      # Simulate the random effects.
      groups_re = np.sqrt(groups_var) * np.random.normal(
          size=n // group_size
      )
      level1_re = np.sqrt(level1_var) * np.random.normal(
          size=n // level1_size
      )
      level2_re = np.sqrt(level2_var) * np.random.normal(
          size=n // level2_size
      )
  
      # Simulate the response variable
      y = (
          groups_re[groups_ix]
          + level1_re[level1_ix]
          + level2_re[level2_ix]
      )
      y += np.sqrt(resid_var) * np.random.normal(size=n)
  
      # Put everything into a dataframe.
      df = pd.DataFrame(xmat, columns=[f"x{j}" for j in range(p)])
      df["y"] = y + xmat[:, 0] - xmat[:, 3]
      df["groups_ix"] = groups_ix
      df["level1_ix"] = level1_ix
      df["level2_ix"] = level2_ix
  
      # Fit the model
      cs = sm.cov_struct.Nested()
      dep_fml = "0 + level1_ix + level2_ix"
      gee = sm.GEE.from_formula(
          "y ~ x0 + x1 + x2 + x3 + x4",
          cov_struct=cs,
          dep_data=dep_fml,
          groups="groups_ix",
          data=df,
      )
      model = gee.fit()
  
      return ModelWithResults(
          model=model, alg=gee, inference_dataframe=df
      )
  
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 11 for calling a global function with example usage: gee_model() and returns: ModelWithResults