class_docstr: Implementation of ``Beta`` via ``Gamma``.
  
  This naive implementation has stochastic reparameterized gradients, which
  have higher variance than PyTorch's ``Beta`` implementation.
class_docstr_tok: ["Implementation","of","``","Beta","``","via","``","Gamma","``",".","This","naive","implementation","has","stochastic","reparameterized","gradients",",","which","have","higher","variance","than","PyTorch","'s","``","Beta","``","implementation","."]
code: def __init__(
      self, concentration1, concentration0, validate_args=None
  ):
      super().__init__(
          concentration1, concentration0, validate_args=validate_args
      )
      alpha_beta = torch.stack([concentration1, concentration0], -1)
      self._gamma = Gamma(alpha_beta, torch.ones_like(alpha_beta))
  
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 NaiveBeta object from inherited base classes: Beta with example: obj = NaiveBeta(concentration1, concentration0, validate_args)