class_docstr: Naive Marsaglia & Tsang rejection sampler for standard Gamma distibution.
  This assumes `concentration >= 1` and does not boost `concentration` or augment shape.
code: def propose_log_prob(self, value):
      v = value / self._d
      result = -self._d.log()
      y = v.pow(1 / 3)
      result -= torch.log(3 * y**2)
      x = (y - 1) / self._c
      result -= self._c.log()
      result += Normal(
          torch.zeros_like(self.concentration),
          torch.ones_like(self.concentration),
      ).log_prob(x)
      return result
  
imports: import torch
  from pyro.distributions.rejector import Rejector
  from pyro.distributions.score_parts import ScoreParts
  from pyro.distributions.torch import Beta, Dirichlet, Gamma, Normal
  from pyro.distributions.util import copy_docs_from, weakmethod
label_desc: Use image node_id 4 for calling the RejectionStandardGamma obj's underlying member method code with example usage: obj.propose_log_prob(value) and returns: result