class_docstr: Abstract base class for all Gaussian process models.
  
  Parameters
  ----------
  configspace : ConfigurationSpace
  kernel : Kernel
      Kernel which is used for the Gaussian process.
  instance_features : dict[str, list[int | float]] | None, defaults to None
      Features (list of int or floats) of the instances (str). The features are incorporated into the X data,
      on which the model is trained on.
  pca_components : float, defaults to 7
      Number of components to keep when using PCA to reduce dimensionality of instance features.
  seed : int
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docstr: Returns all priors.
docstr_tok: ["Returns","all","priors","."]
code: def _get_all_priors(
      self,
      add_bound_priors: bool = True,
      add_soft_bounds: bool = False,
  ) -> list[list[AbstractPrior]]:
      """Returns all priors."""
      # Obtain a list of all priors for each tunable hyperparameter of the kernel
      all_priors = []
      to_visit = []
      to_visit.append(self._gp.kernel.k1)
      to_visit.append(self._gp.kernel.k2)
  
      while len(to_visit) > 0:
          current_param = to_visit.pop(0)
          if isinstance(current_param, KernelOperator):
              to_visit.insert(0, current_param.k1)
              to_visit.insert(1, current_param.k2)
              continue
          elif isinstance(current_param, Kernel):
              hps = current_param.hyperparameters
              assert len(hps) == 1
  
              hp = hps[0]
              if hp.fixed:
                  continue
  
              bounds = hps[0].bounds
              for i in range(hps[0].n_elements):
                  priors_for_hp = []
  
                  if current_param.prior is not None:
                      priors_for_hp.append(current_param.prior)
  
                  if add_bound_priors:
                      if add_soft_bounds:
                          priors_for_hp.append(
                              SoftTopHatPrior(
                                  lower_bound=bounds[i][0],
                                  upper_bound=bounds[i][1],
                                  seed=self._rng.randint(0, 2**20),
                                  exponent=2,
                              )
                          )
                      else:
                          priors_for_hp.append(
                              TophatPrior(
                                  lower_bound=bounds[i][0],
                                  upper_bound=bounds[i][1],
                                  seed=self._rng.randint(0, 2**20),
                              )
                          )
                  all_priors.append(priors_for_hp)
  
      return all_priors
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any
  import numpy
  import sklearn.gaussian_process
  from ConfigSpace import ConfigurationSpace
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import Kernel, KernelOperator
  from smac.model.abstract_model import AbstractModel
  from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
  from smac.model.gaussian_process.priors.tophat_prior import SoftTopHatPrior, TophatPrior
label_desc: Use image node_id 6 for calling the AbstractGaussianProcess obj's underlying member method code with example usage: obj._get_all_priors(add_bound_priors, add_soft_bounds) and returns: all_priors