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 __call__(self, *args, **kwargs):
      return super(Gamma, self).__call__(*args, **kwargs)
  
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 3 for calling the GammaPrior obj's underlying member method code with example usage: obj.__call__() and returns: super