class_docstr: Gamma Prior parameterized by concentration and rate
  
  pdf(x) = beta^alpha / Gamma(alpha) * x^(alpha - 1) * exp(-beta * x)
  
  were alpha > 0 and beta > 0 are the concentration and rate parameters, respectively.
class_docstr_tok: ["Gamma","Prior","parameterized","by","concentration","and","rate","pdf","(","x",")","=","beta^alpha","\/","Gamma","(","alpha",")","*","x^","(","alpha","-","1",")","*","exp","(","-beta","*","x",")","were","alpha",">","0","and","beta",">","0","are","the","concentration","and","rate","parameters",",","respectively","."]
code: def __init__(
      self, concentration, rate, validate_args=False, transform=None
  ):
      TModule.__init__(self)
      Gamma.__init__(
          self,
          concentration=concentration,
          rate=rate,
          validate_args=validate_args,
      )
      _bufferize_attributes(self, ("concentration", "rate"))
      self._transform = transform
  
imports: import torch
  from torch.distributions import Gamma, HalfCauchy, HalfNormal, LogNormal, MultivariateNormal, Normal, Uniform
  from torch.nn import Module
  from .prior import Prior
  from .utils import _bufferize_attributes, _del_attributes
label_desc: Use image node_id 1 to create a new GammaPrior object from inherited base classes: Prior, Gamma with example: obj = GammaPrior(concentration, rate, validate_args, transform)