code: def get_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 = {}
      ref_group_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 = " with ++span-red-init++{bias_metric_value}X++span-red-end++ Disparity".format(
              bias_metric_value="%.2f" % float(row[bias_metric])
          )
  
          ref_group_dict[row["attribute_name"]] = (
              " (with reference group as **"
              + row[ref_group_col]
              + "**)"
          )
  
          sentence2 = "{group_metric_name} of this group is {group_metric_value} compared to {ref_group_metric_value} for the reference group {ref_group_value}.".format(
              group_metric_name=names[group_metric],
              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(
                  "##tooltip-start-title##"
                  + sentence2
                  + "##tooltip-end-title##"
                  + row["attribute_value"]
                  + "##tooltip-end-anchor##"
                  + sentence
              )
          except KeyError:
              false_dict[row["attribute_name"]] = set()
              false_dict[row["attribute_name"]].add(
                  "##tooltip-start-title##"
                  + sentence2
                  + "##tooltip-end-title##"
                  + row["attribute_value"]
                  + "##tooltip-end-anchor##"
                  + sentence
              )
      if false_df.empty:
          cellref = "++span-green-init++Based on the fairness threshold used, all groups passed the audit for this metric.++span-green-end++\n\n"
      else:
          cellref = ""
          for key in false_dict.keys():
              cellref += (
                  "**For {attribute_name}**".format(attribute_name=key)
                  + ref_group_dict[key]
                  + "##br##&emsp;&emsp;&emsp;"
              )
              cellref += (
                  "##br##&emsp;&emsp;&emsp;".join(false_dict[key])
                  + " ##br##  ##br##"
              )
  
      return cellref
  
imports: import datetime
  import logging
  import pandas
  from markdown2 import markdown
  from tabulate import tabulate
label_desc: Use image node_id 11 for calling a global function with example usage: get_false_text(group_value_df, fairness_metric, fairness_measures_depend) and returns: cellref