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 __init__(
      self, concentration1, concentration0, boost=1, validate_args=None
  ):
      super().__init__(
          concentration1, concentration0, validate_args=validate_args
      )
      alpha_beta = torch.stack([concentration1, concentration0], -1)
      self._gamma = ShapeAugmentedGamma(
          alpha_beta, torch.ones_like(alpha_beta), boost
      )
  
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 1 to create a new ShapeAugmentedBeta object from inherited base classes: Beta with example: obj = ShapeAugmentedBeta(concentration1, concentration0, boost, validate_args)