code: def fairness_check(
      object_storage_url,
      object_storage_username,
      object_storage_password,
      data_bucket_name,
      result_bucket_name,
      model_id,
      feature_testset_path="processed_data/X_test.npy",
      label_testset_path="processed_data/y_test.npy",
      protected_label_testset_path="processed_data/p_test.npy",
      model_class_file="model.py",
      model_class_name="model",
      favorable_label=0.0,
      unfavorable_label=1.0,
      privileged_groups=[{"race": 0.0}],
      unprivileged_groups=[{"race": 4.0}],
  ):
      url = re.compile(r"https?://")
      cos = Minio(
          url.sub("", object_storage_url),
          access_key=object_storage_username,
          secret_key=object_storage_password,
          secure=False,
      )  # Local Minio server won't have HTTPS
  
      dataset_filenamex = "X_test.npy"
      dataset_filenamey = "y_test.npy"
      dataset_filenamep = "p_test.npy"
      weights_filename = "model.pt"
      model_files = model_id + "/_submitted_code/model.zip"
  
      cos.fget_object(
          data_bucket_name, feature_testset_path, dataset_filenamex
      )
      cos.fget_object(
          data_bucket_name, label_testset_path, dataset_filenamey
      )
      cos.fget_object(
          data_bucket_name,
          protected_label_testset_path,
          dataset_filenamep,
      )
      cos.fget_object(
          result_bucket_name,
          model_id + "/" + weights_filename,
          weights_filename,
      )
      cos.fget_object(result_bucket_name, model_files, "model.zip")
  
      # Load PyTorch model definition from the source code.
      zip_ref = zipfile.ZipFile("model.zip", "r")
      zip_ref.extractall("model_files")
      zip_ref.close()
  
      modulename = "model_files." + model_class_file.split(".")[
          0
      ].replace("-", "_")
  
      """
      We required users to define where the model class is located or follow
      some naming convention we have provided.
      """
      model_class = getattr(
          importlib.import_module(modulename), model_class_name
      )
  
      # load & compile model
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      model = model_class().to(device)
      model.load_state_dict(
          torch.load(weights_filename, map_location=device)
      )
  
      """Load the necessary labels and protected features for fairness check"""
  
      x_test = np.load(dataset_filenamex)
      y_test = np.load(dataset_filenamey)
      p_test = np.load(dataset_filenamep)
  
      _, y_pred = evaluate(model, x_test, y_test)
  
      """Calculate the fairness metrics"""
  
      original_test_dataset = dataset_wrapper(
          outcome=y_test,
          protected=p_test,
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
          favorable_label=favorable_label,
          unfavorable_label=unfavorable_label,
      )
      plain_predictions_test_dataset = dataset_wrapper(
          outcome=y_pred,
          protected=p_test,
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
          favorable_label=favorable_label,
          unfavorable_label=unfavorable_label,
      )
  
      classified_metric_nodebiasing_test = ClassificationMetric(
          original_test_dataset,
          plain_predictions_test_dataset,
          unprivileged_groups=unprivileged_groups,
          privileged_groups=privileged_groups,
      )
      TPR = classified_metric_nodebiasing_test.true_positive_rate()
      TNR = classified_metric_nodebiasing_test.true_negative_rate()
      bal_acc_nodebiasing_test = 0.5 * (TPR + TNR)
  
      print(
          "#### Plain model - without debiasing - classification metrics on test set"
      )
  
      metrics = {
          "Classification accuracy": classified_metric_nodebiasing_test.accuracy(),
          "Balanced classification accuracy": bal_acc_nodebiasing_test,
          "Statistical parity difference": classified_metric_nodebiasing_test.statistical_parity_difference(),
          "Disparate impact": classified_metric_nodebiasing_test.disparate_impact(),
          "Equal opportunity difference": classified_metric_nodebiasing_test.equal_opportunity_difference(),
          "Average odds difference": classified_metric_nodebiasing_test.average_odds_difference(),
          "Theil index": classified_metric_nodebiasing_test.theil_index(),
          "False negative rate difference": classified_metric_nodebiasing_test.false_negative_rate_difference(),
      }
      print("metrics: ", metrics)
      return metrics
  
file_path: AIF360/mlops/kubeflow/bias_detector_pytorch/src/fairness.py
imports: import os
  from aif360.datasets import BinaryLabelDataset
  from aif360.metrics import ClassificationMetric
  import numpy
  import argparse
  import pandas
  from minio import Minio
  import json
  import zipfile
  import importlib
  import re
  import torch
  import torch.utils.data
  from torch.autograd import Variable
label_desc: Use image node_id 3 for calling a global function with example usage: fairness_check(object_storage_url, object_storage_username, object_storage_password, data_bucket_name, result_bucket_name, model_id, feature_testset_path, label_testset_path, protected_label_testset_path, model_class_file, model_class_name, favorable_label, unfavorable_label, privileged_groups, unprivileged_groups) and returns: metrics