class_docstr: Abstract base class to define the interface for priors of Gaussian process hyperparameters.
  
  This class is adapted from RoBO:
  
  Klein, A. and Falkner, S. and Mansur, N. and Hutter, F.
  RoBO: A Flexible and Robust Bayesian Optimization Framework in Python
  In: NIPS 2017 Bayesian Optimization Workshop
  
  Note
  ----
  Whenever lnprob or the gradient is computed for a scalar input, we use math.* rather than np.*.
  
  Parameters
  ----------
  seed : int, defaults to 0
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docstr: Returns the meta data of the created object.
docstr_tok: ["Returns","the","meta","data","of","the","created","object","."]
code: def meta(self) -> dict[str, Any]:
      """Returns the meta data of the created object."""
      return {
          "name": self.__class__.__name__,
          "seed": self._seed,
      }
  
file_path: SMAC3/smac/model/gaussian_process/priors/abstract_prior.py
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any
  import numpy
label_desc: Use image node_id 2 for calling the AbstractPrior obj's underlying member method code with example usage: obj.meta() and returns: dict