code: def _sample_noisy(self, comp, vals):
      def logprob(hypers):
          mean = hypers[0]
          amp2 = hypers[1]
          noise = hypers[2]
  
          # This is pretty hacky, but keeps things sane.
          if mean > np.max(vals) or mean < np.min(vals):
              return -np.inf
  
          if amp2 < 0 or noise < 0:
              return -np.inf
  
          cov = amp2 * (
              self.cov_func(self.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), vals - mean)
          lp = -np.sum(np.log(np.diag(chol))) - 0.5 * np.dot(
              vals - mean, solve
          )
  
          # Roll in noise horseshoe prior.
          lp += np.log(np.log(1 + (self.noise_scale / noise) ** 2))
          # lp -= 0.5*(np.log(noise)/self.noise_scale)**2
  
          # Roll in amplitude lognormal prior
          lp -= 0.5 * (np.log(amp2) / self.amp2_scale) ** 2
  
          return lp
  
      hypers = util.slice_sample(
          np.array([self.mean, self.amp2, self.noise]),
          logprob,
          compwise=False,
      )
      self.mean = hypers[0]
      self.amp2 = hypers[1]
      self.noise = hypers[2]
  
imports: import os
  from ..None import gp
  import sys
  from ..None import util
  import tempfile
  import numpy
  import numpy.random
  import scipy.linalg
  import scipy.stats
  import scipy.optimize
  import pickle
  from ..helpers import *
  from ..Locker import *
  import logging
label_desc: Use image node_id 14 for calling the GPEIperSecChooser obj's underlying member method code with example usage: obj._sample_noisy(comp, vals) and returns: lp, unknown, unknown