code: def get_final_metric(
      bias_df, overall_auc, POWER=-5, OVERALL_MODEL_WEIGHT=0.25
  ):
      bias_score = np.average(
          [
              power_mean(bias_df[SUBGROUP_AUC], POWER),
              power_mean(bias_df[BPSN_AUC], POWER),
              power_mean(bias_df[BNSP_AUC], POWER),
          ]
      )
      return (OVERALL_MODEL_WEIGHT * overall_auc) + (
          (1 - OVERALL_MODEL_WEIGHT) * bias_score
      )
  
file_path: detoxify/model_eval/compute_bias_metric.py
imports: import argparse
  import json
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score
  from utils import compute_auc, compute_subgroup_auc
label_desc: Use image node_id 8 for calling a global function with example usage: get_final_metric(bias_df, overall_auc, POWER, OVERALL_MODEL_WEIGHT) and returns: unknown