code: def create_warning_section(info_dict):
      """Creates the warning section for model summary.
  
      The following warnings are possible to be included:
  
          Condition number is too big.
          F-ratio and p-value on regularized methods.
          R-squared and F-ratio in glm.
          Zero coefficients in non-sparse regression methods due to degenerate columns.
  
      Parameters
      ----------
      info_dict : `dict`
          The dictionary returned by
          `~greykite.algo.common.model_summary_utils.get_info_dict_lm`.
          or
          `~greykite.algo.common.model_summary_utils.get_info_dict_tree`.
  
      Returns
      -------
      content : `str`
          The model warnings section.
      """
      if info_dict["model_type"] == "lm":
          content = ""
          if info_dict["condition_number"] > 1000:
              content += (
                  f"WARNING: the condition number is large, {'{:.2e}'.format(info_dict['condition_number'])}. "
                  f"This might indicate that there are strong multicollinearity "
                  f"or other numerical problems."
              )
              content += "\n"
          if info_dict["fit_algorithm"] in [
              "ridge",
              "lasso",
              "lars",
              "lasso_lars",
              "sgd",
              "elastic_net",
          ]:
              content += (
                  "WARNING: the F-ratio and its p-value on regularized methods might be misleading, "
                  "they are provided only for reference purposes."
              )
              content += "\n"
          if info_dict["fit_algorithm"] in ["statsmodels_glm"]:
              content += (
                  "WARNING: the R-squared and F-statistics on glm might be misleading, "
                  "they are provided only for reference purposes."
              )
              content += "\n"
          if len(info_dict["nonzero_index"]) < info_dict[
              "n_feature"
          ] and info_dict["fit_algorithm"] in [
              "linear",
              "ridge",
              "statsmodels_ols",
              "statsmodels_wls",
              "statsmodels_gls",
              "statsmodels_glm",
          ]:
              content += (
                  "WARNING: the following columns have estimated coefficients equal to zero, "
                  f"while {info_dict['fit_algorithm']} is not supposed to have zero estimates. "
                  f"This is probably because these columns are degenerate in the design matrix. "
                  f"Make sure these columns do not have constant values."
                  + "\n"
                  + f"{[col for i, col in enumerate(info_dict['pred_cols']) if i not in info_dict['nonzero_index']]}"
              )
              content += "\n"
          if len(info_dict["degenerate_index"]) > 1:
              content += (
                  "WARNING: the following columns are degenerate, do you really want to include them in your model? "
                  "This may cause some of them to show unrealistic significance. Consider using the `drop_degenerate` transformer."
                  + "\n"
                  + f"{[col for i, col in enumerate(info_dict['pred_cols']) if i in info_dict['degenerate_index']]}"
              )
              content += "\n"
          if content == "":
              content = None
      else:
          content = None
      return content
  
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 29 for calling a global function with example usage: create_warning_section(info_dict) and returns: content