docstr: Returns a kernel for the Gaussian Process surrogate model.
  
  The kernel is a composite of kernels depending on the type of hyperparameters:
  categorical (HammingKernel), continuous (Matern), and noise kernels (White).
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code: def get_kernel(scenario: Scenario) -> kernels.Kernel:
      """Returns a kernel for the Gaussian Process surrogate model.
  
      The kernel is a composite of kernels depending on the type of hyperparameters:
      categorical (HammingKernel), continuous (Matern), and noise kernels (White).
      """
      types, _ = get_types(scenario.configspace, instance_features=None)
      cont_dims = np.where(np.array(types) == 0)[0]
      cat_dims = np.where(np.array(types) != 0)[0]
  
      if (len(cont_dims) + len(cat_dims)) != len(
          scenario.configspace.get_hyperparameters()
      ):
          raise ValueError(
              "The inferred number of continuous and categorical hyperparameters "
              "must equal the total number of hyperparameters. Got "
              f"{(len(cont_dims) + len(cat_dims))} != {len(scenario.configspace.get_hyperparameters())}."
          )
  
      # Constant Kernel
      cov_amp = ConstantKernel(
          2.0,
          constant_value_bounds=(np.exp(-10), np.exp(2)),
          prior=LogNormalPrior(
              mean=0.0,
              sigma=1.0,
              seed=scenario.seed,
          ),
      )
  
      # Continuous / Categorical Kernels
      exp_kernel, ham_kernel = 0.0, 0.0
      if len(cont_dims) > 0:
          exp_kernel = MaternKernel(
              np.ones([len(cont_dims)]),
              [
                  (
                      np.exp(-6.754111155189306),
                      np.exp(0.0858637988771976),
                  )
                  for _ in range(len(cont_dims))
              ],
              nu=2.5,
              operate_on=cont_dims,
          )
      if len(cat_dims) > 0:
          ham_kernel = HammingKernel(
              np.ones([len(cat_dims)]),
              [
                  (
                      np.exp(-6.754111155189306),
                      np.exp(0.0858637988771976),
                  )
                  for _ in range(len(cat_dims))
              ],
              operate_on=cat_dims,
          )
  
      # Noise Kernel
      noise_kernel = WhiteKernel(
          noise_level=1e-8,
          noise_level_bounds=(np.exp(-25), np.exp(2)),
          prior=HorseshoePrior(scale=0.1, seed=scenario.seed),
      )
  
      # Continuous and categecorical HPs
      if len(cont_dims) > 0 and len(cat_dims) > 0:
          kernel = cov_amp * (exp_kernel * ham_kernel) + noise_kernel
  
      # Only continuous HPs
      elif len(cont_dims) > 0 and len(cat_dims) == 0:
          kernel = cov_amp * exp_kernel + noise_kernel
  
      # Only categorical HPs
      elif len(cont_dims) == 0 and len(cat_dims) > 0:
          kernel = cov_amp * ham_kernel + noise_kernel
  
      else:
          raise ValueError(
              "The number of continuous and categorical hyperparameters must be greater than zero."
          )
  
      return kernel
  
imports: from __future__ import annotations
  import numpy
  import sklearn.gaussian_process.kernels
  from ConfigSpace import Configuration
  from smac.acquisition.function.expected_improvement import EI
  from smac.acquisition.maximizer.local_and_random_search import LocalAndSortedRandomSearch
  from smac.facade.abstract_facade import AbstractFacade
  from smac.initial_design.sobol_design import SobolInitialDesign
  from smac.intensifier.intensifier import Intensifier
  from smac.model.gaussian_process.abstract_gaussian_process import AbstractGaussianProcess
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.kernels import ConstantKernel, HammingKernel, MaternKernel, WhiteKernel
  from smac.model.gaussian_process.mcmc_gaussian_process import MCMCGaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
  from smac.random_design.probability_design import ProbabilityRandomDesign
  from smac.runhistory.encoder.encoder import RunHistoryEncoder
  from smac.scenario import Scenario
  from smac.utils.configspace import get_types
label_desc: Use image node_id 3 for calling the BlackBoxFacade obj's underlying member method code with example usage: obj.get_kernel(scenario) and returns: kernel