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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code: def __init__(
      self,
      configspace: ConfigurationSpace,
      kernel: Kernel,
      instance_features: dict[str, list[int | float]] | None = None,
      pca_components: int | None = 7,
      seed: int = 0,
  ):
      super().__init__(
          configspace=configspace,
          instance_features=instance_features,
          pca_components=pca_components,
          seed=seed,
      )
  
      self._kernel = kernel
      self._gp = self._get_gaussian_process()
  
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 1 to create a new AbstractGaussianProcess object from inherited base classes: AbstractModel with example: obj = AbstractGaussianProcess(configspace, kernel, instance_features, pca_components, seed)