code: def norm_score(pred):
      """norm score
      pred {key: [[x1,y1,x2,y2,s]]}
      """
  
      max_score = -1
      min_score = 2
  
      for _, k in pred.items():
          for _, v in k.items():
              if len(v) == 0:
                  continue
              _min = np.min(v[:, -1])
              _max = np.max(v[:, -1])
              max_score = max(_max, max_score)
              min_score = min(_min, min_score)
  
      diff = max_score - min_score
      for _, k in pred.items():
          for _, v in k.items():
              if len(v) == 0:
                  continue
              v[:, -1] = (v[:, -1] - min_score).astype(
                  np.float64
              ) / diff
      return pred
  
imports: from __future__ import absolute_import
  import os
  import tqdm
  import pickle
  import datetime
  import argparse
  import numpy
  from scipy.io import loadmat
label_desc: Use image node_id 10 for calling a global function with example usage: norm_score(pred) and returns: pred