code: def _sample_ls(self, comp, vals):
      def logprob(ls):
          if np.any(ls < 0) or np.any(ls > self.max_ls):
              return -np.inf
  
          cov = self.amp2 * (
              self.cov_func(ls, comp, None)
              + 1e-6 * np.eye(comp.shape[0])
          ) + self.noise * np.eye(comp.shape[0])
          chol = spla.cholesky(cov, lower=True)
          solve = spla.cho_solve((chol, True), vals - self.mean)
          lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(
              vals - self.mean, solve
          )
          return lp
  
      self.ls = util.slice_sample(self.ls, logprob, compwise=True)
  
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 14 for calling the GPEIOptChooser obj's underlying member method code with example usage: obj._sample_ls(comp, vals) and returns: lp, unknown