code: def _build_kernel_for_domain(
      domain,
      dom_prefix,
      kernel_scale,
      gp_cts_hps,
      gp_dscr_hps,
      other_gp_params,
      options,
      kernel_ordering,
      kernel_params_for_each_domain,
  ):
      """Builds the kernel for the domain."""
      kernel_list = []
      # Iterate through each domain and build the corresponding kernel
      for dom_idx, dom, kernel_type in zip(
          range(domain.num_domains),
          domain.list_of_domains,
          kernel_ordering,
      ):
          dom_type = dom.get_type().lower()
          if kernel_type == "" or kernel_type is None:
              # If none is specified, use the one given in options
              kernel_type = _get_kernel_type_from_options(
                  dom_type, "dom", options
              )
          if kernel_type == "default":
              kernel_type = get_default_kernel_type(dom.get_type())
          if dom_type in [
              "euclidean",
              "integral",
              "prod_discrete_numeric",
              "discrete_euclidean",
          ]:
              curr_kernel_hyperparams = (
                  _prep_kernel_hyperparams_for_euc_int_kernels(
                      kernel_type, dom, dom_prefix, options
                  )
              )
              use_same_bw, _, esp_kernel_type, _ = _get_euc_int_options(
                  dom_type, "dom", options
              )
              if (
                  hasattr(other_gp_params, "add_gp_groupings")
                  and other_gp_params.add_gp_groupings is not None
              ):
                  add_gp_groupings = other_gp_params.add_gp_groupings[
                      dom_idx
                  ]
              else:
                  add_gp_groupings = None
              (
                  curr_kernel,
                  gp_cts_hps,
                  gp_dscr_hps,
              ) = get_euclidean_integral_gp_kernel_with_scale(
                  kernel_type,
                  1.0,
                  curr_kernel_hyperparams,
                  gp_cts_hps,
                  gp_dscr_hps,
                  use_same_bw,
                  add_gp_groupings,
                  esp_kernel_type,
              )
          elif dom_type == "prod_discrete":
              curr_kernel_hyperparams = (
                  _prep_kernel_hyperparams_for_discrete_kernels(
                      kernel_type, dom, dom_prefix, options
                  )
              )
              (
                  curr_kernel,
                  gp_cts_hps,
                  gp_dscr_hps,
              ) = get_discrete_kernel(
                  kernel_type,
                  curr_kernel_hyperparams,
                  gp_cts_hps,
                  gp_dscr_hps,
              )
          elif dom_type == "neural_network":
              curr_kernel_hyperparams = (
                  _prep_kernel_hyperparams_for_nn_kernels(
                      kernel_type,
                      dom,
                      kernel_params_for_each_domain[dom_idx],
                  )
              )
              (
                  curr_kernel,
                  gp_cts_hps,
                  gp_dscr_hps,
              ) = get_neural_network_kernel(
                  kernel_type,
                  curr_kernel_hyperparams,
                  gp_cts_hps,
                  gp_dscr_hps,
              )
          else:
              raise NotImplementedError(
                  (
                      "Not implemented _child_build_gp for dom_type "
                      + "%s yet."
                  )
                  % (dom_type)
              )
          kernel_list.append(curr_kernel)
      return (
          CartesianProductKernel(kernel_scale, kernel_list),
          gp_cts_hps,
          gp_dscr_hps,
      )
  
imports: from __future__ import print_function
  from argparse import Namespace
  import numpy
  from ..exd import domains
  from ..exd.cp_domain_utils import load_cp_domain_from_config_file
  from .None import gp_core, mf_gp
  from .euclidean_gp import get_euclidean_integral_gp_kernel_with_scale, prep_euclidean_integral_kernel_hyperparams
  from .kernel import CartesianProductKernel, HammingKernel
  from ..utils.general_utils import get_idxs_from_list_of_lists
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 10 for calling a global function with example usage: _build_kernel_for_domain(domain, dom_prefix, kernel_scale, gp_cts_hps, gp_dscr_hps, other_gp_params, options, kernel_ordering, kernel_params_for_each_domain) and returns: CartesianProductKernel,  gp_cts_hps,  gp_dscr_hps