code: def get_old_false_text(
      group_value_df, fairness_metric, fairness_measures_depend
  ):
      names = {
          "fpr": "false positive rate",
          "fnr": "false negative rate",
          "fdr": "false discovery rate",
          "for": "false omission rate",
          "ppr": "predicted positive ratio",
          "pprev": "predicted prevalence (percentage of positives in the group)",
      }
      group_value_df = group_value_df.round(2)
      group_value_df = group_value_df.applymap(str)
      false_df = group_value_df.loc[
          group_value_df[fairness_metric] == "False"
      ]
      bias_metric = fairness_measures_depend[fairness_metric]
      group_metric = bias_metric.replace("_disparity", "")
      ref_group_col = group_metric + "_ref_group_value"
      text_detail = ""
      false_dict = {}
      for index, row in false_df.iterrows():
          ref_group_row = group_value_df.loc[
              (
                  group_value_df["attribute_name"]
                  == row["attribute_name"]
              )
              & (
                  group_value_df["attribute_value"]
                  == row[ref_group_col]
              )
          ]
          sentence = (
              ": **{bias_metric_value}**% "
              'of the {group_metric_name} of the reference group "{ref_group_value}",'
              " corresponding to a difference of {group_metric_value} vs {ref_group_metric_value}.".format(
                  group_metric_name=names[group_metric],
                  bias_metric_value="%.0f"
                  % (float(row[bias_metric]) * 100),
                  ref_group_value=row[ref_group_col],
                  group_metric_value=row[group_metric],
                  ref_group_metric_value=ref_group_row[
                      group_metric
                  ].values[0],
              )
          )
          try:
              false_dict[row["attribute_name"]].add(
                  "["
                  + row["attribute_value"]
                  + "](#false-positive-parity)"
                  + sentence
              )
          except KeyError:
              false_dict[row["attribute_name"]] = set()
              false_dict[row["attribute_name"]].add(
                  "["
                  + row["attribute_value"]
                  + "](#false-positive-parity)"
                  + sentence
              )
  
      if false_df.empty:
          cellref = (
              "##green##Based on the fairness threshold used, the percentage of selected elements from "
              "each group is not disparate to the percentage of selected elements of the respective reference group.\n\n"
          )
      else:
          cellref = ""
          for key in false_dict.keys():
              cellref += "**{attribute_name}:** ##br##&emsp;&emsp;&emsp;".format(
                  attribute_name=key
              )
              cellref += (
                  "##br##&emsp;&emsp;&emsp;".join(false_dict[key])
                  + " ##br##"
              )
  
      return cellref
  
imports: import datetime
  import logging
  import pandas
  from markdown2 import markdown
  from tabulate import tabulate
label_desc: Use image node_id 10 for calling a global function with example usage: get_old_false_text(group_value_df, fairness_metric, fairness_measures_depend) and returns: cellref