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: Normalize data to zero mean unit standard deviation.
  
  Parameters
  ----------
  y : np.ndarray
      Target values for the Gaussian process.
  
  Returns
  -------
  normalized_y : np.ndarray
      Normalized y values.
docstr_tok: ["Normalize","data","to","zero","mean","unit","standard","deviation",".","Parameters","--","--","--","--","--","y",":","np.ndarray","Target","values","for","the","Gaussian","process",".","Returns","--","--","--","-","normalized_y",":","np.ndarray","Normalized","y","values","."]
code: def _normalize(self, y: np.ndarray) -> np.ndarray:
      """Normalize data to zero mean unit standard deviation.
  
      Parameters
      ----------
      y : np.ndarray
          Target values for the Gaussian process.
  
      Returns
      -------
      normalized_y : np.ndarray
          Normalized y values.
      """
      self.mean_y_ = np.mean(y)
      self.std_y_ = np.std(y)
  
      if self.std_y_ == 0:
          self.std_y_ = 1
  
      return (y - self.mean_y_) / self.std_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 4 for calling the AbstractGaussianProcess obj's underlying member method code with example usage: obj._normalize(y) and returns: unknown