code: def compute_patch_similarity():
      """
      Compute the feature vectors for each patch using the trained neural network.
      """
      global xs, ys, xs_image, ys_image
  
      print("Computing features")
      ys = features(encoded, encoded=True)
      xs = features(original, encoded=False)
  
      model = Model()
      ckpt = objax.io.Checkpoint("saved", keep_ckpts=0)
      ckpt.restore(model.vars())
  
      xs_image = model.encode(xs)
      ys_image = model.decode(ys)
      assert xs.shape[0] == xs_image.shape[0]
      print("Done")
  
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 11 for calling a global function with example usage: compute_patch_similarity() without return types