code: def audit_description(configs, group_value_df):
      supported_name = {
          "Statistical Parity": "**Equal Parity** - Ensure all protected groups are have equal representation in the selected set.",
          "Impact Parity": "**Proportional Parity** - Ensure all protected groups are selected proportional to their "
          "percentage of the population.",
          "FPR Parity": "**False Positive Rate Parity** - Ensure all protected groups have the same false positive "
          "rates as the reference group).",
          "FDR Parity": "**False Discovery Rate Parity** - Ensure all protected groups have equally proportional false "
          "positives within the selected set (compared to the reference group).",
          "FNR Parity": "**False Negative Rate Parity** - Ensure all protected groups have the same false negative rates ("
          "as the reference group).",
          "FOR Parity": "**False Omission Rate Parity** - Ensure all protected groups have equally proportional false "
          "negatives within the non-selected set (compared to the reference group).",
      }
  
      supported_order = [
          "Statistical Parity",
          "Impact Parity",
          "FPR Parity",
          "FDR Parity",
          "FNR Parity",
          "FOR Parity",
      ]
  
      ref_group = {
          "predefined": "Custom group - The reference groups you selected for each attribute will be used  to "
          "calculate relative disparities in this audit.",
          "majority": "Majority group - The largest groups on each attribute will be used as baseline to calculate "
          "relative "
          "disparities in this audit.",
          "min_metric": "Automatically select, for each bias metric, the group on each attribute that has the "
          "lower "
          "value, to be used as baseline to calculate relative disparities in this audit.",
      }
  
      raw = {"column1": [], "column2": []}
  
      raw["column1"].append("**Audit Date:**")
      raw["column2"].append(datetime.date.today().strftime("%d %b %Y"))
      raw["column1"].append("**Data Audited:**")
      raw["column2"].append(
          "{:.0f} rows".format(
              group_value_df["total_entities"].values[0]
          )
      )
      raw["column1"].append("**Attributes Audited:**")
      raw["column2"].append(
          ", ".join(group_value_df["attribute_name"].unique())
      )
      raw["column1"].append("**Audit Goal(s):**")
      measures = [
          supported_name[m]
          for m in supported_order
          if m in configs.fair_measures_requested
      ]
      raw["column2"].append("\n".join(measures) + "\n")
      raw["column1"].append("**Reference Groups:**")
  
      raw["column2"].append(ref_group[configs.ref_groups_method])
      raw["column1"].append("**Fairness Threshold:**")
      thresh = (
          "{:.0f}%. If disparity for a group is within {"
          ":.0f}% and "
          "{"
          ":.0f}% of "
          " the value of the reference group on a group metric (e.g. False "
          "Positive Rate), this audit will pass. "
          "".format(
              float(configs.fairness_threshold) * 100,
              float(configs.fairness_threshold) * 100,
              float(1.0 / configs.fairness_threshold) * 100,
          )
      )
      raw["column2"].append(thresh)
  
      df = pd.DataFrame(raw, columns=["column1", "column2"])
      desc_table = tabulate(
          df[["column1", "column2"]],
          headers="keys",
          tablefmt="pipe",
          showindex="never",
          numalign="left",
      )
  
      return desc_table
  
imports: import datetime
  import logging
  import pandas
  from markdown2 import markdown
  from tabulate import tabulate
label_desc: Use image node_id 14 for calling a global function with example usage: audit_description(configs, group_value_df) and returns: desc_table