class_docstr: Implementation of ``Dirichlet`` via ``Gamma``.
  
  This naive implementation has stochastic reparameterized gradients, which
  have higher variance than PyTorch's ``Dirichlet`` implementation.
class_docstr_tok: ["Implementation","of","``","Dirichlet","``","via","``","Gamma","``",".","This","naive","implementation","has","stochastic","reparameterized","gradients",",","which","have","higher","variance","than","PyTorch","'s","``","Dirichlet","``","implementation","."]
code: def __init__(self, concentration, validate_args=None):
      super().__init__(concentration)
      self._gamma = Gamma(
          concentration,
          torch.ones_like(concentration),
          validate_args=validate_args,
      )
  
file_path: pyro/pyro/distributions/testing/naive_dirichlet.py
imports: import torch
  from pyro.distributions.torch import Beta, Dirichlet, Gamma
  from pyro.distributions.util import copy_docs_from
label_desc: Use image node_id 1 to create a new NaiveDirichlet object from inherited base classes: Dirichlet with example: obj = NaiveDirichlet(concentration, validate_args)