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 __init__(
      self,
      sigma: float,
      mean: float = 0,
      seed: int = 0,
  ):
      super().__init__(seed=seed)
  
      if mean != 0:
          raise NotImplementedError(mean)
  
      self._sigma = sigma
      self._sigma_square = sigma**2
      self._mean = mean
      self._sqrt_2_pi = np.sqrt(2 * np.pi)
  
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 1 to create a new LogNormalPrior object from inherited base classes: AbstractPrior with example: obj = LogNormalPrior(sigma, mean, seed)