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_log_probability(self, theta: float) -> float:
      if theta <= self._mean:
          return -1e25
      else:
          rval = -((math.log(theta) - self._mean) ** 2) / (
              2 * self._sigma_square
          ) - math.log(self._sqrt_2_pi * self._sigma * theta)
          return rval
  
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 4 for calling the LogNormalPrior obj's underlying member method code with example usage: obj._get_log_probability(theta) and returns: unknown, rval