class_docstr: A fitter for GPs in multi-fidelity optimisation. 
class_docstr_tok: ["A","fitter","for","GPs","in","multi-fidelity","optimisation","."]
docstr: Samples continous and discrete hyper-parameters for rand_exp_sampling. 
code: def _sample_cts_dscr_hps_for_rand_exp_sampling(self):
      """Samples continous and discrete hyper-parameters for rand_exp_sampling."""
      if not self.options.domain_use_additive_gp:
          return super(
              EuclideanMFGPFitter, self
          )._sample_cts_dscr_hps_for_rand_exp_sampling()
      else:
          return sample_cts_dscr_hps_for_rand_exp_sampling_in_add_model(
              self.hp_tune_max_evals,
              self.cts_hp_bounds,
              self.domain_dim,
              self.dscr_hp_vals,
              self.domain_add_group_size_idx_in_dscr_hp_vals,
              self._tuning_objective,
          )
  
imports: from __future__ import division
  from argparse import Namespace
  import numpy
  from .None import gp_core, mf_gp
  from .None import kernel
  from ..utils.ancillary_utils import get_list_of_floats_as_str
  from ..utils.general_utils import get_sublist_from_indices, map_to_bounds
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.oper_utils import random_sample_from_discrete_domain
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 17 for calling the EuclideanMFGPFitter obj's underlying member method code with example usage: obj._sample_cts_dscr_hps_for_rand_exp_sampling() and returns: super, sample_cts_dscr_hps_for_rand_exp_sampling_in_add_model