is a relaxed version of the Bernoulli distribution, so the values
   are in (0, 1), and has reparametrizable samples.
Example:
  >>> m = RelaxedBernoulli(torch.tensor([2.2]),
  ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
  >>> m.sample()
  tensor([ 0.2951,  0.3442,  0.8918,  0.9021])

Parameters:
      * temperature (Tensor) -- relaxation temperature
  * **probs** (*Number**, **Tensor*) -- the probability of
    sampling *1*

  * **logits** (*Number**, **Tensor*) -- the log-odds of sampling
    *1*

arg_constraints: Dict[str, constraints.Constraint] = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)}
expand(batch_shape, _instance=None)
has_rsample = True
property logits
property probs
support = Interval(lower_bound=0.0, upper_bound=1.0)
property temperature
LogitRelaxedBernoulli