code: def recursivels_model():
      # Recursive Least Squares
      dta = sm.datasets.copper.load_pandas().data
      dta.index = pd.date_range("1951-01-01", "1975-01-01", freq="AS")
      endog = dta.WORLDCONSUMPTION
  
      # To the regressors in the dataset, we add a column of ones for an intercept
      exog = sm.add_constant(
          dta[
              [
                  "COPPERPRICE",
                  "INCOMEINDEX",
                  "ALUMPRICE",
                  "INVENTORYINDEX",
              ]
          ]  # pylint: disable=unsubscriptable-object
      )
      rls = sm.RecursiveLS(endog, exog)
      model = rls.fit()
  
      inference_dataframe = pd.DataFrame(
          [["1951-01-01", "1975-01-01"]], columns=["start", "end"]
      )
      return ModelWithResults(
          model=model, alg=rls, inference_dataframe=inference_dataframe
      )
  
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 8 for calling a global function with example usage: recursivels_model() and returns: ModelWithResults