code: def __init__(
      self,
      model_file,
      data_path,
      subset,
      batch_size=64,
      force_reload=False,
      restore_embs=None,
  ):
      (
          templates,
          medias,
          p1,
          p2,
          label,
          img_names,
          landmarks,
          face_scores,
      ) = extract_IJB_data_11(
          data_path, subset, force_reload=force_reload
      )
      if model_file != None:
          if model_file.endswith(".h5"):
              interf_func = keras_model_interf(model_file)
          elif model_file.endswith(".pth") or model_file.endswith(
              ".pt"
          ):
              interf_func = Torch_model_interf(model_file)
          elif model_file.endswith(".onnx") or model_file.endswith(
              ".ONNX"
          ):
              interf_func = ONNX_model_interf(model_file)
          else:
              interf_func = Mxnet_model_interf(model_file)
          self.embs, self.embs_f = get_embeddings(
              interf_func, img_names, landmarks, batch_size=batch_size
          )
      elif restore_embs != None:
          print(">>>> Reload embeddings from:", restore_embs)
          aa = np.load(restore_embs)
          if "embs" in aa and "embs_f" in aa:
              self.embs, self.embs_f = aa["embs"], aa["embs_f"]
          else:
              print(
                  "ERROR: %s NOT containing embs / embs_f"
                  % restore_embs
              )
              exit(1)
          print(">>>> Done.")
      self.data_path, self.subset, self.force_reload = (
          data_path,
          subset,
          force_reload,
      )
      self.templates, self.medias, self.p1, self.p2, self.label = (
          templates,
          medias,
          p1,
          p2,
          label,
      )
      self.face_scores = face_scores.astype(self.embs.dtype)
  
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 1 to create a new IJB_test object  with example: obj = IJB_test(model_file, data_path, subset, batch_size, force_reload, restore_embs)