code: def parse_arguments(argv):
      import argparse
  
      default_save_result_name = (
          "IJB_result/{model_name}_{subset}_{type}.npz"
      )
      parser = argparse.ArgumentParser(
          formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      parser.add_argument(
          "-m",
          "--model_file",
          type=str,
          default=None,
          help="Saved model, keras h5 / pytorch jit pth / onnx / mxnet",
      )
      parser.add_argument(
          "-d",
          "--data_path",
          type=str,
          default="./",
          help="Dataset path containing IJBB and IJBC sub folder",
      )
      parser.add_argument(
          "-s",
          "--subset",
          type=str,
          default="IJBC",
          help="Subset test target, could be IJBB / IJBC",
      )
      parser.add_argument(
          "-b",
          "--batch_size",
          type=int,
          default=128,
          help="Batch size for get_embeddings",
      )
      parser.add_argument(
          "-R",
          "--save_result",
          type=str,
          default=default_save_result_name,
          help="Filename for saving / restore result",
      )
      parser.add_argument(
          "-L",
          "--save_label",
          action="store_true",
          help="Save label data, useful for plot only",
      )
      parser.add_argument(
          "-E",
          "--save_embeddings",
          action="store_true",
          help="Save embeddings data",
      )
      parser.add_argument(
          "-B",
          "--is_bunch",
          action="store_true",
          help="Run all 8 tests N{0,1}D{0,1}F{0,1}",
      )
      parser.add_argument(
          "-N",
          "--is_one_2_N",
          action="store_true",
          help="Run 1:N test instead of 1:1",
      )
      parser.add_argument(
          "-F",
          "--force_reload",
          action="store_true",
          help="Force reload, instead of using cache",
      )
      parser.add_argument(
          "-P",
          "--plot_only",
          nargs="*",
          type=str,
          help="Plot saved results, Format 1 2 3 or 1, 2, 3 or *.npy",
      )
      args = parser.parse_known_args(argv)[0]
  
      if args.plot_only != None and len(args.plot_only) != 0:
          # Plot only
          from glob2 import glob
  
          score_files = []
          for ss in args.plot_only:
              score_files.extend(glob(ss.replace(",", "").strip()))
          args.plot_only = score_files
      elif (
          args.model_file == None
          and args.save_result == default_save_result_name
      ):
          print("Please provide -m MODEL_FILE, see `--help` for usage.")
          exit(1)
      elif args.model_file != None:
          if (
              args.model_file.endswith(".h5")
              or args.model_file.endswith(".pth")
              or args.model_file.endswith(".pt")
              or args.model_file.endswith(".onnx")
          ):
              # Keras model file "model.h5", pytorch model ends with `.pth` or `.pt`, onnx model ends with `.onnx`
              model_name = os.path.splitext(
                  os.path.basename(args.model_file)
              )[0]
          else:
              # MXNet model file "models/r50-arcface-emore/model,1"
              model_name = os.path.basename(
                  os.path.dirname(args.model_file)
              )
  
          if args.save_result == default_save_result_name:
              type = "1N" if args.is_one_2_N else "11"
              args.save_result = default_save_result_name.format(
                  model_name=model_name, subset=args.subset, type=type
              )
      return args
  
imports: import os
  import cv2
  import numpy
  import pandas
  from tqdm import tqdm
  from skimage import transform
  from sklearn.preprocessing import normalize
  from sklearn.metrics import roc_curve, auc
label_desc: Use image node_id 13 for calling a global function with example usage: parse_arguments(argv) and returns: args