code: def compute_metrics(
      dataset_true,
      dataset_pred,
      unprivileged_groups,
      privileged_groups,
      disp=True,
  ):
      """Compute the key metrics"""
      classified_metric_pred = ClassificationMetric(
          dataset_true,
          dataset_pred,
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
      )
      metrics = OrderedDict()
      metrics["Balanced accuracy"] = 0.5 * (
          classified_metric_pred.true_positive_rate()
          + classified_metric_pred.true_negative_rate()
      )
      metrics[
          "Statistical parity difference"
      ] = classified_metric_pred.statistical_parity_difference()
      metrics[
          "Disparate impact"
      ] = classified_metric_pred.disparate_impact()
      metrics[
          "Average odds difference"
      ] = classified_metric_pred.average_odds_difference()
      metrics[
          "Equal opportunity difference"
      ] = classified_metric_pred.equal_opportunity_difference()
      metrics["Theil index"] = classified_metric_pred.theil_index()
  
      if disp:
          for k in metrics:
              print("%s = %.4f" % (k, metrics[k]))
  
      return metrics
  
label_desc: Use image node_id 1 for calling a global function with example usage: compute_metrics(dataset_true, dataset_pred, unprivileged_groups, privileged_groups, disp) and returns: metrics