code: def test_multistage_forecast_model_template_with_lagged_regressor(
      df, forecast_config
  ):
      forecaster = Forecaster()
      forecast_config.model_components_param.custom[
          "multistage_forecast_configs"
      ][0].model_components.lagged_regressors[
          "lagged_regressor_dict"
      ] = [
          {
              "regressor": {
                  "lag_dict": {"orders": [12]},
                  "series_na_fill_func": lambda s: s.bfill().ffill(),
              }
          }
      ]
      forecast_result = forecaster.run_forecast_config(
          df=df, config=forecast_config
      )
      assert forecast_result.backtest is not None
      assert forecast_result.grid_search is not None
      assert forecast_result.forecast is not None
  
      assert len(forecast_result.model[-1].models) == 2
      # Checks the forecast horizons in each model.
      assert (
          forecast_result.model[-1].models[0].forecast_horizon == 1
      )  # daily model
      assert (
          forecast_result.model[-1].models[1].forecast_horizon == 12
      )  # hourly model
  
      # Checks the autoregression orders are as expected.
      assert (
          "y_lag1"
          in forecast_result.model[-1]
          .models[0]
          .model_dict["x_mat"]
          .columns
      )
      assert (
          "y_lag12"
          in forecast_result.model[-1]
          .models[1]
          .model_dict["x_mat"]
          .columns
      )
  
      # Checks that the regressor column is included.
      assert (
          "regressor_lag12"
          in forecast_result.model[-1]
          .models[0]
          .model_dict["x_mat"]
          .columns
      )
  
      # Checks the forecast is not NAN
      assert len(
          forecast_result.forecast.df_test[PREDICTED_COL].dropna()
      ) == len(forecast_result.forecast.df_test)
      assert len(
          forecast_result.backtest.df_test[PREDICTED_COL].dropna()
      ) == len(forecast_result.backtest.df_test)
  
imports: import datetime
  import numpy
  import pytest
  from testfixtures import LogCapture
  from greykite.common.constants import LOGGER_NAME
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import MetadataParam
  from greykite.framework.templates.autogen.forecast_config import ModelComponentsParam
  from greykite.framework.templates.forecaster import Forecaster
  from greykite.framework.templates.multistage_forecast_template import MultistageForecastTemplate
  from greykite.framework.templates.multistage_forecast_template_config import SILVERKITE_TWO_STAGE
  from greykite.framework.templates.multistage_forecast_template_config import MultistageForecastTemplateConfig
  from greykite.framework.templates.simple_silverkite_template import SimpleSilverkiteTemplate
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.uncertainty.uncertainty_methods import UncertaintyMethodEnum
label_desc: Use image node_id 16 for calling a global function with example usage: test_multistage_forecast_model_template_with_lagged_regressor(df, forecast_config) without return types