code: def train_preprocessor():
      print("train preproc", preproc_str)
      d = xt.shape[1]
      t0 = time.time()
      if preproc_str.startswith("OPQ"):
          fi = preproc_str[3:-1].split("_")
          m = int(fi[0])
          dout = int(fi[1]) if len(fi) == 2 else d
          preproc = faiss.OPQMatrix(d, m, dout)
      elif preproc_str.startswith("PCAR"):
          dout = int(preproc_str[4:-1])
          preproc = faiss.PCAMatrix(d, dout, 0, True)
      else:
          assert False
      preproc.train(sanitize(xt[:1000000]))
      print("preproc train done in %.3f s" % (time.time() - t0))
      return preproc
  
imports: from __future__ import print_function
  import numpy
  import time
  import os
  import sys
  import faiss
  import re
  from multiprocessing.pool import ThreadPool
  from datasets import ivecs_read
label_desc: Use image node_id 10 for calling a global function with example usage: train_preprocessor() and returns: preproc