class_docstr: Compound distribution comprising of a beta-binomial pair. The probability of
  success (``probs`` for the :class:`~pyro.distributions.Binomial` distribution)
  is unknown and randomly drawn from a :class:`~pyro.distributions.Beta` distribution
  prior to a certain number of Bernoulli trials given by ``total_count``.
  
  :param concentration1: 1st concentration parameter (alpha) for the
      Beta distribution.
  :type concentration1: float or torch.Tensor
  :param concentration0: 2nd concentration parameter (beta) for the
      Beta distribution.
  :type concentration0: float or torch.Tensor
  :param total_count: Number of Bernoulli trials.
  :type total_count: float or torch.Tensor
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code: def enumerate_support(self, expand=True):
      total_count = int(self.total_count.max())
      if not self.total_count.min() == total_count:
          raise NotImplementedError(
              "Inhomogeneous total count not supported by `enumerate_support`."
          )
      values = torch.arange(
          1 + total_count,
          dtype=self.concentration1.dtype,
          device=self.concentration1.device,
      )
      values = values.view((-1,) + (1,) * len(self._batch_shape))
      if expand:
          values = values.expand((-1,) + self._batch_shape)
      return values
  
imports: import numbers
  import torch
  from torch.distributions.utils import broadcast_all
  from pyro.ops.special import log_beta, log_binomial
  from .None import constraints
  from .torch import Beta, Binomial, Dirichlet, Gamma, Multinomial, Poisson
  from .torch_distribution import TorchDistribution
  from .util import broadcast_shape
label_desc: Use image node_id 9 for calling the BetaBinomial obj's underlying member method code with example usage: obj.enumerate_support(expand) and returns: values