class_docstr: Implementation of ``Dirichlet`` via ``ShapeAugmentedGamma``.
  
  This naive implementation has stochastic reparameterized gradients, which
  have higher variance than PyTorch's ``Dirichlet`` implementation.
code: def __init__(self, concentration, boost=1, validate_args=None):
      super().__init__(concentration, validate_args=validate_args)
      self._gamma = ShapeAugmentedGamma(
          concentration, torch.ones_like(concentration), 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 ShapeAugmentedDirichlet object from inherited base classes: Dirichlet with example: obj = ShapeAugmentedDirichlet(concentration, boost, validate_args)