code: def test_score_function(daily_data_with_reg):
      """Checks score function without null model, with regressors"""
      model = BaseSilverkiteEstimator()
      train_df = daily_data_with_reg["train_df"]
      test_df = daily_data_with_reg["test_df"]
  
      # every subclass `fit` follows these steps
      model.fit(
          X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL
      )
      silverkite = SilverkiteForecast()
      model.model_dict = silverkite.forecast(
          df=train_df,
          time_col=cst.TIME_COL,
          value_col=cst.VALUE_COL,
          origin_for_time_vars=None,
          extra_pred_cols=["ct1", "regressor1", "regressor2"],
          train_test_thresh=None,
          training_fraction=None,
          fit_algorithm="linear",
          fit_algorithm_params=None,
          daily_event_df_dict=None,
          changepoints_dict=None,
          fs_components_df=pd.DataFrame(
              {
                  "name": ["tod", "tow", "conti_year"],
                  "period": [24.0, 7.0, 1.0],
                  "order": [3, 3, 5],
                  "seas_names": ["daily", "weekly", "yearly"],
              }
          ),
          autoreg_dict=None,
          min_admissible_value=None,
          max_admissible_value=None,
          uncertainty_dict=None,
      )
      model.finish_fit()
  
      score = model.score(test_df, test_df[cst.VALUE_COL])
      pred_df = model.predict(test_df)
      assert list(pred_df.columns) == [cst.TIME_COL, cst.PREDICTED_COL]
      assert score == pytest.approx(
          mean_squared_error(
              pred_df[cst.PREDICTED_COL], test_df[cst.VALUE_COL]
          )
      )
      assert score == pytest.approx(4.6, rel=1e-1)
  
imports: import datetime
  import numpy
  import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from testfixtures import LogCapture
  import greykite.common.constants
  from greykite.algo.forecast.silverkite.forecast_silverkite import SilverkiteForecast
  from greykite.common.data_loader import DataLoader
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
  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.base_silverkite_estimator import BaseSilverkiteEstimator
  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 11 for calling a global function with example usage: test_score_function(daily_data_with_reg) without return types