class_docstr: Implementation of gamma prior.
  
  f(x) = (x-loc)**(a-1) * e**(-(x-loc)) * (1/scale)**a / gamma(a)
  
  Parameters
  ----------
  a : float
      The shape parameter. Must be greater than 0.
  scale : float
      The scale parameter (1/scale corresponds to parameter p in canonical form). Must be greather than 0.
  loc : float
      Mean parameter for the distribution.
  seed : int, defaults to 0
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code: def __init__(
      self,
      a: float,
      scale: float,
      loc: float,
      seed: int = 0,
  ):
      super().__init__(seed=seed)
      assert a > 0
      assert scale > 0
  
      self._a = a
      self._loc = loc
      self._scale = scale
  
file_path: SMAC3/smac/model/gaussian_process/priors/gamma_prior.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import scipy.stats
  from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
label_desc: Use image node_id 1 to create a new GammaPrior object from inherited base classes: AbstractPrior with example: obj = GammaPrior(a, scale, loc, seed)