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(self, sample_shape=torch.Size()):
      # Marsaglia & Tsang's x == Naesseth's epsilon`
      x = torch.randn(
          sample_shape + self.concentration.shape,
          dtype=self.concentration.dtype,
          device=self.concentration.device,
      )
      y = 1.0 + self._c * x
      v = y * y * y
      return (self._d * v).clamp_(1e-30, 1e30)
  
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 3 for calling the RejectionStandardGamma obj's underlying member method code with example usage: obj.propose(sample_shape) and returns: unknown