used due to the similarity with the Bernoulli. See [1] for more
   details.
Example:
  >>> m = ContinuousBernoulli(torch.tensor([0.3]))
  >>> m.sample()
  tensor([ 0.2538])

Parameters:
      * probs (Number, Tensor) -- (0,1) valued parameters
  * **logits** (*Number**, **Tensor*) -- real valued parameters
    whose sigmoid matches 'probs'

[1] The continuous Bernoulli: fixing a pervasive error in
   variational autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS
   2019. https://arxiv.org/abs/1907.06845
arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(value)
log_prob(value)
property logits
property mean
property param_shape
property probs
rsample(sample_shape=torch.Size([]))
sample(sample_shape=torch.Size([]))
property stddev
support = Interval(lower_bound=0.0, upper_bound=1.0)