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 log probability of theta. This method exponentiates theta and calls `self._get_log_probability`.
  
  Warning
  -------
  Theta must be on a log scale!
  
  Parameters
  ----------
  theta : float
      Hyperparameter configuration in log space.
  
  Returns
  -------
  float
      The log probability of theta
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code: def get_log_probability(self, theta: float) -> float:
      """Returns the log probability of theta. This method exponentiates theta and calls `self._get_log_probability`.
  
      Warning
      -------
      Theta must be on a log scale!
  
      Parameters
      ----------
      theta : float
          Hyperparameter configuration in log space.
  
      Returns
      -------
      float
          The log probability of theta
      """
      return self._get_log_probability(np.exp(theta))
  
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 4 for calling the AbstractPrior obj's underlying member method code with example usage: obj.get_log_probability(theta) and returns: self