code: def compute_bnsp_auc(df, subgroup, label, model_name):
      """Computes the AUC of the within-subgroup positive examples and the background negative examples."""
      subgroup_positive_examples = df[df[subgroup] & df[label]]
      non_subgroup_negative_examples = df[~df[subgroup] & ~df[label]]
      examples = subgroup_positive_examples.append(
          non_subgroup_negative_examples
      )
      return compute_auc(examples[label], examples[model_name])
  
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 2 for calling a global function with example usage: compute_bnsp_auc(df, subgroup, label, model_name) and returns: compute_auc