class_docstr: A fitter for GPs in multi-fidelity optimisation. 
class_docstr_tok: ["A","fitter","for","GPs","in","multi-fidelity","optimisation","."]
docstr: Sets up the domain kernel as ESP kernel. 
code: def _domain_esp_kernel_setup(self):
      """Sets up the domain kernel as ESP kernel."""
      if (
          hasattr(self.options, "domain_bandwidth_log_bounds")
          and self.options.domain_bandwidth_log_bounds is not None
      ):
          self.domain_bandwidth_log_bounds = (
              self.options.domain_bandwidth_log_bounds
          )
      else:
          self.domain_bandwidth_log_bounds = (
              self._get_bandwidth_log_bounds(
                  self.domain_dim,
                  self.ZX_std_norm,
                  self.options.domain_use_same_bandwidth,
              )
          )
      self.cts_hp_bounds.extend(self.domain_bandwidth_log_bounds)
      for _ in range(self.domain_dim):
          self.param_order.append(["dim_bandwidths", "cts"])
      if (
          self.options.domain_esp_kernel_type == "matern"
          and self.options.domain_esp_matern_nu < 0
      ):
          self.dscr_hp_vals.append([0.5, 1.5, 2.5])
          self.param_order.append(["nu", "dscr"])
  
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 13 for calling the EuclideanMFGPFitter obj's underlying member method code with example usage: obj._domain_esp_kernel_setup() without return types