class_docstr: Class for fitting Gaussian processes. 
class_docstr_tok: ["Class","for","fitting","Gaussian","processes","."]
docstr: Optimises the continuous hyper-parameters for the given discrete hyper-params.
  Can be overridden by a child class if necessary.
docstr_tok: ["Optimises","the","continuous","hyper-parameters","for","the","given","discrete","hyper-params",".","Can","be","overridden","by","a","child","class","if","necessary","."]
code: def _optimise_cts_hps_for_given_dscr_hps(self, given_dscr_hps):
      """Optimises the continuous hyper-parameters for the given discrete hyper-params.
      Can be overridden by a child class if necessary.
      """
      cts_tuning_obj = lambda arg: self._tuning_objective(
          arg, list(given_dscr_hps)
      )
      opt_cts_val, opt_cts_hps, _ = self.cts_hp_optimise(
          cts_tuning_obj, self.hp_tune_max_evals
      )
      return opt_cts_val, opt_cts_hps, None
  
imports: from __future__ import division
  from argparse import Namespace
  from itertools import product
  import sys
  import numpy
  from ..distributions.model import Model
  from ..distributions import continuous
  from ..distributions import discrete
  from ..utils.general_utils import stable_cholesky, draw_gaussian_samples, project_symmetric_to_psd_cone, solve_lower_triangular, solve_upper_triangular
  from ..utils.oper_utils import direct_ft_maximise, pdoo_maximise, random_maximise, random_sample_cts_dscr
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 14 for calling the GPFitter obj's underlying member method code with example usage: obj._optimise_cts_hps_for_given_dscr_hps(given_dscr_hps) and returns: opt_cts_val,  opt_cts_hps,  None