code: def add_model_coef_df_tree(info_dict):
      """Adds coefficient summary df to `info_dict` for tree models.
  
      Only coefficient summary information will be added in this function.
      A series of these functions are used in a flow to get all information needed
      in tree model summary. The flow is
      `~greykite.algo.common.model_summary_utils.create_info_dict_tree`,
      `~greykite.algo.common.model_summary_utils.add_model_params_tree`,
      `~greykite.algo.common.model_summary_utils.add_model_coef_df_tree`.
      For flow implementation, see
      `~greykite.algo.common.model_summary_utils.get_info_dict_tree`.
  
      Parameters
      ----------
      info_dict : `dict`
          The information dictionary returned by
          `~greykite.algo.common.model_summary_utils.add_model_params_tree`.
  
      Returns
      -------
      info_dict : `dict`
          The information dictionary with the following keys added:
  
              "coef_summary_df" : `pandas.DataFrame`
                  The model summary df with the following columns:
  
                      "Pred_col" : `str`
                          The predictor names.
                      "Feature importance" : `float`
                          The impurity-based feature importance.
                      "Importance rank" : `int`
                          The rank of feature importance.
      """
      feature_importance = info_dict["ml_model"].feature_importances_
      # Gets ranks
      temp = feature_importance.argsort()
      ranks = np.empty_like(temp)
      ranks[temp] = np.arange(len(feature_importance))
      coef_df = pd.DataFrame(
          {
              "Pred_col": info_dict["pred_cols"],
              "Feature importance": feature_importance,
              "Importance rank": len(feature_importance)
              - ranks,  # Reverse order
          }
      )
      info_dict["coef_summary_df"] = coef_df
      return info_dict
  
imports: import numpy
  import pandas
  import statsmodels
  import statsmodels.api
  from scipy.stats import f
  from scipy.stats import norm
  from scipy.stats import t
  from sklearn.base import clone
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from greykite.algo.common.col_name_utils import INTERCEPT
  from greykite.algo.common.col_name_utils import simplify_pred_cols
label_desc: Use image node_id 21 for calling a global function with example usage: add_model_coef_df_tree(info_dict) and returns: info_dict