code: def recover_global_matching_first():
      """
      Recover the global matching of original to encoded images by doing
      an all-pairs matching problem
      """
      global global_matching, ys_image, encoded
  
      matrix = []
      p = mp.Pool(96)
      xs_image_ = np.array(xs_image)
      ys_image_ = np.array(ys_image)
  
      matrix = p.map(match, [(x, ys_image_) for x in xs_image_])
      matrix = np.array(matrix).reshape(
          (xs_image.shape[0], xs_image.shape[0])
      )
  
      row, col = scipy.optimize.linear_sum_assignment(-np.array(matrix))
      global_matching = np.argsort(col)
      print("glob", list(global_matching))
  
      p.close()
  
imports: import collections
  import hashlib
  import time
  import multiprocessing
  import torch
  import numpy
  import torch.nn
  import scipy.stats
  import matplotlib.pyplot
  from PIL import Image
  import jax
  import jax.numpy
  import objax
  import scipy.optimize
  import numpy
  import multiprocessing
label_desc: Use image node_id 13 for calling a global function with example usage: recover_global_matching_first() without return types