code: def calculate_overall_auc(df, model_name):
      true_labels = df[TOXICITY_COLUMN]
      predicted_labels = df[model_name]
      return roc_auc_score(true_labels, predicted_labels)
  
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 6 for calling a global function with example usage: calculate_overall_auc(df, model_name) and returns: roc_auc_score