class_docstr: Implementation of ``rate`` via ``ShapeAugmentedGamma``.
  
  This naive implementation has stochastic reparameterized gradients, which
  have higher variance than PyTorch's ``rate`` implementation.
code: def rsample(self, sample_shape=torch.Size()):
      gammas = self._gamma.rsample(sample_shape)
      probs = gammas / gammas.sum(-1, True)
      return probs[..., 0]
  
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 ShapeAugmentedBeta obj's underlying member method code with example usage: obj.rsample(sample_shape) and returns: probs