class_docstr: Implements the log normal prior.
  
  Parameters
  ----------
  sigma : float
      Specifies the standard deviation of the normal distribution.
  mean : float
      Specifies the mean of the normal distribution.
  seed : int, defaults to 0
class_docstr_tok: ["Implements","the","log","normal","prior",".","Parameters","--","--","--","--","--","sigma",":","float","Specifies","the","standard","deviation","of","the","normal","distribution",".","mean",":","float","Specifies","the","mean","of","the","normal","distribution",".","seed",":","int",",","defaults","to","0"]
code: def _get_gradient(self, theta: float) -> float:
      if theta <= 0:
          return 0
      else:
          # Derivative of log(1 / (x * s^2 * sqrt(2 pi)) * exp( - 0.5 * (log(x ) / s^2))^2))
          # This is without the mean!
          return (
              -(self._sigma_square + math.log(theta))
              / (self._sigma_square * (theta))
              * theta
          )
  
file_path: SMAC3/smac/model/gaussian_process/priors/log_normal_prior.py
imports: from __future__ import annotations
  from typing import Any
  import math
  import numpy
  from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
label_desc: Use image node_id 5 for calling the LogNormalPrior obj's underlying member method code with example usage: obj._get_gradient(theta) and returns: int, unknown