code: def recover_better_local_permutation():
      """
      Given a set of encoded images, return a new encoding, but better!
      """
      global encoded, ys
  
      # Now instead of pairing all images to image 0, we compute the mean l2 vector
      # and then pair all images onto the mean vector. Slightly more noise resistant.
      p = mp.Pool(96)
      target = encoded.mean(0)
      local_perm = p.map(match_sub, [(target, e) for e in encoded])
      local_perm = np.array(local_perm)
  
      # Probably we didn't change by much, generally <0.1%
      print(
          "improved changed by",
          np.mean(local_perm != np.arange(local_perm.shape[1])),
      )
  
      encoded_perm = []
  
      for i in range(len(encoded)):
          encoded_perm.append(encoded[i][np.argsort(local_perm[i])])
  
      encoded = np.array(encoded_perm)
  
      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 10 for calling a global function with example usage: recover_better_local_permutation() without return types