code: def multistage_forecast_configs():
      configs = [
          MultistageForecastTemplateConfig(
              train_length="30D",
              fit_length=None,
              agg_func="nanmean",
              agg_freq="D",
              model_template="SILVERKITE",
              model_components=ModelComponentsParam(
                  seasonality={
                      "yearly_seasonality": 12,
                      "quarterly_seasonality": 5,
                      "monthly_seasonality": 5,
                      "weekly_seasonality": 4,
                      "daily_seasonality": 0,
                  },
                  growth={"growth_term": "linear"},
                  events={
                      "holidays_to_model_separately": "auto",
                      "holiday_lookup_countries": "auto",
                      "holiday_pre_num_days": 1,
                      "holiday_post_num_days": 1,
                      "holiday_pre_post_num_dict": None,
                      "daily_event_df_dict": None,
                  },
                  changepoints={
                      "changepoints_dict": None,
                      "seasonality_changepoints_dict": None,
                  },
                  autoregression={"autoreg_dict": "auto"},
                  regressors={"regressor_cols": []},
                  lagged_regressors={"lagged_regressor_dict": None},
                  uncertainty={"uncertainty_dict": None},
                  custom={
                      "fit_algorithm_dict": {
                          "fit_algorithm": "ridge",
                          "fit_algorithm_params": None,
                      },
                      "feature_sets_enabled": "auto",  # "auto" based on data freq and size
                      "max_daily_seas_interaction_order": 0,
                      "max_weekly_seas_interaction_order": 2,
                      "extra_pred_cols": [],
                      "min_admissible_value": None,
                      "max_admissible_value": None,
                      "drop_pred_cols": None,
                      "explicit_pred_cols": None,
                      "regression_weight_col": None,
                      "normalize_method": "zero_to_one",
                  },
              ),
          ),
          MultistageForecastTemplateConfig(
              train_length="7D",
              fit_length=None,
              agg_func="nanmean",
              agg_freq=None,
              model_template="SILVERKITE",
              model_components=ModelComponentsParam(
                  seasonality={
                      "yearly_seasonality": 0,
                      "quarterly_seasonality": 0,
                      "monthly_seasonality": 0,
                      "weekly_seasonality": 0,
                      "daily_seasonality": 12,
                  },
                  growth={"growth_term": None},
                  events={
                      "holidays_to_model_separately": [],
                      "holiday_lookup_countries": [],
                      "holiday_pre_num_days": 0,
                      "holiday_post_num_days": 0,
                      "holiday_pre_post_num_dict": None,
                      "daily_event_df_dict": None,
                  },
                  changepoints={
                      "changepoints_dict": None,
                      "seasonality_changepoints_dict": None,
                  },
                  autoregression={"autoreg_dict": "auto"},
                  regressors={"regressor_cols": []},
                  lagged_regressors={"lagged_regressor_dict": None},
                  uncertainty={"uncertainty_dict": None},
                  custom={
                      "fit_algorithm_dict": {
                          "fit_algorithm": "ridge",
                          "fit_algorithm_params": None,
                      },
                      "feature_sets_enabled": "auto",  # "auto" based on data freq and size
                      "max_daily_seas_interaction_order": 5,
                      "max_weekly_seas_interaction_order": 2,
                      "extra_pred_cols": [],
                      "min_admissible_value": None,
                      "max_admissible_value": None,
                      "drop_pred_cols": None,
                      "explicit_pred_cols": None,
                      "regression_weight_col": None,
                      "normalize_method": "zero_to_one",
                  },
              ),
          ),
      ]
      return configs
  
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 3 for calling a global function with example usage: multistage_forecast_configs() and returns: configs