code: def run_model_test_bunch(self):
      from itertools import product
  
      scores, names = [], []
      for use_norm_score, use_detector_score, use_flip_test in product(
          [True, False], [True, False], [True, False]
      ):
          name = "N{:d}D{:d}F{:d}".format(
              use_norm_score, use_detector_score, use_flip_test
          )
          print(
              ">>>>",
              name,
              use_norm_score,
              use_detector_score,
              use_flip_test,
          )
          names.append(name)
          scores.append(
              self.run_model_test_single(
                  use_flip_test, use_norm_score, use_detector_score
              )
          )
      return scores, names
  
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 3 for calling the IJB_test obj's underlying member method code with example usage: obj.run_model_test_bunch() and returns: scores,  names