code: def test_model_summary(df_pt):
      model = BaseSilverkiteEstimator()
      model.fit(
          X=df_pt.iloc[:100],  # speeds up
          time_col=cst.TIME_COL,
          value_col=cst.VALUE_COL,
      )
      params = {"fit_algorithm": "linear", "training_fraction": 0.8}
      silverkite = SilverkiteForecast()
      model.model_dict = silverkite.forecast(
          df=df_pt.iloc[:100],
          time_col=cst.TIME_COL,
          value_col=cst.VALUE_COL,
          **params,
      )
      model.finish_fit()
      summary = model.summary()
      summary.__str__()
      summary.__repr__()
      assert summary is not None
  
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 17 for calling a global function with example usage: test_model_summary(df_pt) without return types