code: def _sample_constraint_noisy(self, comp, vals):
      def lpProbit(ff, gain=self.constraint_gain):
          probs = sps.norm.cdf(ff * gain)
          probs[probs <= 0] = 1e-12
          probs[probs >= 1] = 1 - 1e-12
          llh = np.sum(
              vals * np.log(probs) + (1 - vals) * np.log(1 - probs)
          )
          if np.any(np.isnan(probs)):
              log(probs)
          return llh
  
      def lpSigmoid(ff, gain=self.constraint_gain):
          probs = 1.0 / (1.0 + np.exp(-gain * ff))
          probs[probs <= 0] = 1e-12
          probs[probs >= 1] = 1 - 1e-12
          llh = np.sum(
              vals * np.log(probs) + (1 - vals) * np.log(1 - probs)
          )
          return llh
  
      def logprob(hypers):
          amp2 = hypers[0]
          ff = hypers[1:]
  
          if amp2 < 0:
              return -np.inf
  
          noise = self.constraint_noise
          cov = amp2 * (
              self.cov_func(self.constraint_ls, comp, None)
              + 1e-6 * np.eye(comp.shape[0])
          ) + noise * np.eye(comp.shape[0])
          chol = spla.cholesky(cov, lower=True)
          solve = spla.cho_solve((chol, True), ff)
          lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(ff, solve)
  
          # Roll in amplitude lognormal prior
          lp -= 0.5 * (np.log(amp2) / self.constraint_amp2_scale) ** 2
  
          lp += lpProbit(ff, self.constraint_gain)
  
          return lp
  
      hypers = util.slice_sample(
          np.hstack((np.array([self.constraint_amp2]), self.ff)),
          logprob,
          compwise=False,
      )
      self.constraint_amp2 = hypers[0]
      self.ff = hypers[1:]
      cov = self.constraint_amp2 * (
          (
              self.cov_func(self.constraint_ls, comp, None)
              + 1e-6 * np.eye(comp.shape[0])
          )
          + self.constraint_noise * np.eye(comp.shape[0])
      )
      chol = spla.cholesky(cov, lower=False)
      ff = self.ff
      for jj in range(50):
          (ff, lpell) = self.elliptical_slice(ff, chol, lpProbit)
      self.ff = ff
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import numpy
  import math
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import pickle
  import matplotlib.pyplot
  import multiprocessing
  import copy
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 20 for calling the GPConstrainedEIChooser obj's underlying member method code with example usage: obj._sample_constraint_noisy(comp, vals) and returns: llh, llh, lp, unknown