code: def test_estimator_get_coef_summary_from_forecaster():
      """Tests model summary for silverkite model with missing values in value_col after everything is setup by Forecaster"""
      dl = DataLoader()
      df_pt = dl.load_peyton_manning()
      config = ForecastConfig().from_dict(
          dict(
              model_template=ModelTemplateEnum.SILVERKITE.name,
              forecast_horizon=10,
              metadata_param=dict(
                  time_col="ts", value_col="y", freq="D"
              ),
              model_components_param=dict(
                  custom={
                      "fit_algorithm_dict": {"fit_algorithm": "linear"}
                  }
              ),
          )
      )
      result = Forecaster().run_forecast_config(
          df=df_pt[:365], config=config  # shortens df to speed up
      )
      summary = result.model[-1].summary()
      x = summary.get_coef_summary(is_intercept=True, return_df=True)
      assert x.shape[0] == 1
      summary.get_coef_summary(is_time_feature=True)
      summary.get_coef_summary(is_event=True)
      summary.get_coef_summary(is_trend=True)
      summary.get_coef_summary(is_interaction=True)
      x = summary.get_coef_summary(is_lag=True)
      assert x is None
      x = summary.get_coef_summary(
          is_trend=True,
          is_seasonality=False,
          is_interaction=False,
          return_df=True,
      )
      assert all([":" not in col for col in x["Pred_col"].tolist()])
      assert "ct1" in x["Pred_col"].tolist()
      assert "sin1_ct1_yearly" not in x["Pred_col"].tolist()
      x = summary.get_coef_summary(return_df=True)
      assert x.shape[0] == summary.info_dict["coef_summary_df"].shape[0]
  
imports: import sys
  import warnings
  from enum import Enum
  import numpy
  import pandas
  import pytest
  from testfixtures import LogCapture
  from greykite.common.constants import LOGGER_NAME
  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.data_loader import DataLoader
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.framework.templates.autogen.forecast_config import ComputationParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationMetricParam
  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.model_templates import ModelTemplate
  from greykite.framework.templates.model_templates import ModelTemplateEnum
  from greykite.framework.templates.prophet_template import ProphetTemplate
  from greykite.framework.templates.silverkite_template import SilverkiteTemplate
  from greykite.framework.templates.simple_silverkite_template import SimpleSilverkiteTemplate
  from greykite.framework.templates.simple_silverkite_template_config import SILVERKITE_COMPONENT_KEYWORDS
  from greykite.framework.templates.simple_silverkite_template_config import SimpleSilverkiteTemplateOptions
  from greykite.framework.utils.framework_testing_utils import assert_basic_pipeline_equal
  from greykite.framework.utils.framework_testing_utils import assert_forecast_pipeline_result_equal
  from greykite.framework.utils.framework_testing_utils import check_forecast_pipeline_result
  from greykite.framework.utils.result_summary import summarize_grid_search_results
label_desc: Use image node_id 11 for calling a global function with example usage: test_estimator_get_coef_summary_from_forecaster() without return types