code: def add_model_coef_df_lm(info_dict):
      """Adds the tests and confidence intervals for estimated coefficients
         to `info_dict` for linear models.
  
      Only tests and confidence intervals information will be added in this function.
      A series of these functions are used in a flow to get all information needed
      in linear model summary. The flow is
      `~greykite.algo.common.model_summary_utils.create_info_dict_lm`,
      `~greykite.algo.common.model_summary_utils.add_model_params_lm`,
      `~greykite.algo.common.model_summary_utils.add_model_df_lm`,
      `~greykite.algo.common.model_summary_utils.add_model_ss_lm`,
      `~greykite.algo.common.model_summary_utils.add_beta_var_lm`,
      `~greykite.algo.common.model_summary_utils.add_model_coef_df_lm`,
      `~greykite.algo.common.model_summary_utils.add_model_significance_lm`.
      For flow implementation, see
      `~greykite.algo.common.model_summary_utils.get_info_dict_lm`.
  
      Parameters
      ----------
      info_dict : `dict`
          The information dictionary returned by
          `~greykite.algo.common.model_summary_utils.add_beta_var_lm`.
  
      Returns
      -------
      info_dict : `dict`
          The information dictionary with the following keys added:
  
              "coef_summary_df" : `pandas.DataFrame`
                  The summary df for estimated coefficients.
              "significance_code_legend" : `str`, optional
                  The significance code legend.
      """
      if info_dict["fit_algorithm"] in [
          "statsmodels_ols",
          "statsmodels_wls",
          "statsmodels_gls",
          "linear",
      ]:
          info_dict = get_ls_coef_df(info_dict)
      elif info_dict["fit_algorithm"] in ["statsmodels_glm"]:
          info_dict = get_glm_coef_df(info_dict)
      elif info_dict["fit_algorithm"] in ["ridge"] or (
          info_dict["fit_algorithm"] in ["sgd", "elastic_net"]
          and info_dict["l1_ratio"] == 0
      ):
          info_dict = get_ridge_coef_df(info_dict)
      elif info_dict["fit_algorithm"] in [
          "lasso",
          "lars",
          "lasso_lars",
      ] or (
          info_dict["fit_algorithm"] in ["elastic_net"]
          and info_dict["l1_ratio"] == 1
      ):
          info_dict = get_lasso_coef_df(info_dict)
      elif info_dict["fit_algorithm"] in ["sgd", "elastic_net"]:
          info_dict = get_elasticnet_coef_df(info_dict)
      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 16 for calling a global function with example usage: add_model_coef_df_lm(info_dict) and returns: info_dict