code: def params2():
      autoreg_dict = {
          "lag_dict": {"orders": [7]},
          "agg_lag_dict": {
              "orders_list": [[7, 7 * 2, 7 * 3]],
              "interval_list": [(7, 7 * 2)],
          },
          "series_na_fill_func": lambda s: s.bfill().ffill(),
      }
      lagged_regressor_dict = {
          "regressor1": {
              "lag_dict": {"orders": [1, 2, 3]},
              "agg_lag_dict": {
                  "orders_list": [[7, 7 * 2, 7 * 3]],
                  "interval_list": [(8, 7 * 2)],
              },
              "series_na_fill_func": lambda s: s.bfill().ffill(),
          },
          "regressor2": "auto",
      }
      uncertainty_dict = {
          "uncertainty_method": "simple_conditional_residuals",
          "params": {
              "conditional_cols": ["dow"],
              "quantiles": [0.025, 0.975],
              "quantile_estimation_method": "normal_fit",
              "sample_size_thresh": 5,
              "small_sample_size_method": "std_quantiles",
              "small_sample_size_quantile": 0.98,
          },
      }
      return {
          "origin_for_time_vars": convert_date_to_continuous_time(
              datetime.datetime(2018, 1, 1)
          ),
          "extra_pred_cols": ["ct1", "regressor1", "regressor2"],
          "train_test_thresh": None,
          "training_fraction": None,
          "fit_algorithm_dict": {
              "fit_algorithm": "ridge",
              "fit_algorithm_params": None,
          },
          "daily_event_df_dict": None,
          "changepoints_dict": None,
          "changepoint_detector": None,
          "fs_components_df": pd.DataFrame(
              {
                  "name": ["tow"],
                  "period": [7.0],
                  "order": [3],
                  "seas_names": [None],
              }
          ),
          "autoreg_dict": autoreg_dict,
          "lagged_regressor_dict": lagged_regressor_dict,
          "min_admissible_value": None,
          "max_admissible_value": None,
          "uncertainty_dict": uncertainty_dict,
          "normalize_method": "zero_to_one",
          "adjust_anomalous_dict": None,
          "impute_dict": {
              "func": impute_with_lags,
              "params": {"orders": [7]},
          },
          "regression_weight_col": "ct2",
          "forecast_horizon": 5,
          "simulation_based": True,
      }
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from pandas.testing import assert_frame_equal
  from sklearn.linear_model import SGDRegressor
  from sklearn.metrics import mean_squared_error
  import greykite.common.constants
  from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
  from greykite.common.features.timeseries_impute import impute_with_lags
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import daily_data_reg
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.sklearn.estimator.silverkite_estimator import SilverkiteEstimator
  from greykite.sklearn.estimator.testing_utils import params_component_breakdowns
  from greykite.sklearn.estimator.testing_utils import params_components
label_desc: Use image node_id 2 for calling a global function with example usage: params2() and returns: dict