code: def get_discrete_kernel(
      kernel_type, kernel_hyperparams, gp_cts_hps, gp_dscr_hps
  ):
      """Returns the kernel for discrete hyper-parameters."""
      dim = kernel_hyperparams["dim"]
      if kernel_type == "hamming":
          if (
              dim == 1
              or kernel_hyperparams["disc_hamming_use_same_weight"]
          ):
              dim_wts = np.ones((dim,)) / float(dim)
          elif dim == 2:
              dim_1_wt = gp_cts_hps[0]
              dim_wts = np.array([dim_1_wt, 1 - dim_1_wt])
              gp_cts_hps = gp_cts_hps[1:]
          else:
              dim_wts_unnorm = np.array(gp_cts_hps[0:dim])
              dim_wts_sum = dim_wts_unnorm.sum()
              if dim_wts_sum > 0:
                  dim_wts = dim_wts_unnorm / dim_wts_sum
              else:
                  dim_wts = np.ones((dim,)) / float(dim)
              gp_cts_hps = gp_cts_hps[dim:]
          kern = HammingKernel(dim_wts)
      else:
          raise ValueError(
              'Unknown kernel_type "%s" for discrete spaces.'
              % (kernel_type)
          )
      return kern, 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 14 for calling a global function with example usage: get_discrete_kernel(kernel_type, kernel_hyperparams, gp_cts_hps, gp_dscr_hps) and returns: kern,  gp_cts_hps,  gp_dscr_hps