code: def features(x, encoded):
      """
      Given the original images or the encoded images, generate the
      features to use for the patch similarity function.
      """
      print("start shape", x.shape)
      if len(x.shape) == 3:
          x = x - np.mean(x, axis=0, keepdims=True)
      else:
          # count x 100 x 256 x 768
          print(x[0].shape)
          x = x - np.mean(x, axis=1, keepdims=True)
          # remove per-neural-network dimension
          x = x.reshape((x.shape[0] * x.shape[1],) + x.shape[2:])
      p = mp.Pool(96)
      B = len(x) // 96
      print(1)
      bs = [x[i : i + B] for i in range(0, len(x), B)]
      print(2)
      r = p.map(features_, bs)
      # r = features_(bs[0][:100])
      print(3)
      p.close()
      # r = np.array(r)
      # print('finish',r.shape)
      return np.concatenate(r, axis=0)
  
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 4 for calling a global function with example usage: features(x, encoded) and returns: np