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: Transform zero mean unit standard deviation data into the regular space.
  
  Warning
  -------
  This function should be used after a prediction with the Gaussian process which was
  trained on normalized data.
  
  Parameters
  ----------
  y : np.ndarray
      Normalized data.
  var : np.ndarray | None, defaults to None
      Normalized variance.
  
  Returns
  -------
  untransformed_y : np.ndarray | tuple[np.ndarray, np.ndarray]
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code: def _untransform_y(
      self,
      y: np.ndarray,
      var: np.ndarray | None = None,
  ) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
      """Transform zero mean unit standard deviation data into the regular space.
  
      Warning
      -------
      This function should be used after a prediction with the Gaussian process which was
      trained on normalized data.
  
      Parameters
      ----------
      y : np.ndarray
          Normalized data.
      var : np.ndarray | None, defaults to None
          Normalized variance.
  
      Returns
      -------
      untransformed_y : np.ndarray | tuple[np.ndarray, np.ndarray]
      """
      y = y * self.std_y_ + self.mean_y_
      if var is not None:
          var = var * self.std_y_**2
          return y, var  # type: ignore
  
      return y
  
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 5 for calling the AbstractGaussianProcess obj's underlying member method code with example usage: obj._untransform_y(y, var) and returns: y, y,  var