code: def get_lasso_coef_df(info_dict):
      """Gets the coefficients dataframe for lasso regression models.
  
      The dataframe includes the estimated values, the probability a coefficient is nonzero,
      the significance test p-values, the significance code and 95% confidence intervals.
      The tests and confidence intervals are based on multi sample-splitting techniques.
  
      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`
                  The significance code legend.
      """
      (
          info_dict["coef_summary_df"],
          info_dict["significance_code_legend"],
      ) = get_lasso_coef_df_by_multi_sample_split(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 14 for calling a global function with example usage: get_lasso_coef_df(info_dict) and returns: info_dict