code: def __init__(
      self,
      expt_dir,
      covar="Matern52",
      mcmc_iters=10,
      pending_samples=100,
      noiseless=False,
      burnin=100,
      grid_subset=20,
      use_multiprocessing=True,
  ):
      self.cov_func = getattr(gp, covar)
      self.locker = Locker()
      self.state_pkl = os.path.join(expt_dir, self.__module__ + ".pkl")
      self.stats_file = os.path.join(
          expt_dir, self.__module__ + "_hyperparameters.txt"
      )
      self.mcmc_iters = int(mcmc_iters)
      self.burnin = int(burnin)
      self.needs_burnin = True
      self.pending_samples = int(pending_samples)
      self.D = -1
      self.hyper_iters = 1
      # Number of points to optimize EI over
      self.grid_subset = int(grid_subset)
      self.noiseless = bool(int(noiseless))
      self.hyper_samples = []
  
      self.noise_scale = 0.1  # horseshoe prior
      self.amp2_scale = 1  # zero-mean log normal prior
      self.max_ls = 2  # top-hat prior on length scales
  
      # If multiprocessing fails or deadlocks, set this to False
      self.use_multiprocessing = bool(int(use_multiprocessing))
  
      self.ls = None
      self.amp2 = None
      self.mean = None
      self.noise = None
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import pickle
  import copy
  import numpy
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import multiprocessing
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 1 to create a new GPEIOptChooser object  with example: obj = GPEIOptChooser(expt_dir, covar, mcmc_iters, pending_samples, noiseless, burnin, grid_subset, use_multiprocessing)