code: def process_embeddings(
      embs,
      embs_f=[],
      use_flip_test=True,
      use_norm_score=False,
      use_detector_score=True,
      face_scores=None,
  ):
      print(
          ">>>> process_embeddings: Norm {}, Detect_score {}, Flip {}".format(
              use_norm_score, use_detector_score, use_flip_test
          )
      )
      if use_flip_test and len(embs_f) != 0:
          embs = embs + embs_f
      if use_norm_score:
          embs = normalize(embs)
      if use_detector_score and face_scores is not None:
          embs = embs * np.expand_dims(face_scores, -1)
      return embs
  
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 7 for calling a global function with example usage: process_embeddings(embs, embs_f, use_flip_test, use_norm_score, use_detector_score, face_scores) and returns: embs