LogitRelaxedBernoulli
class torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli(temperature, probs=None, logits=None, validate_args=None)
Bases: "Distribution"
Creates a LogitRelaxedBernoulli distribution parameterized by
   "probs" or "logits" (but not both), which is the logit of a
   RelaxedBernoulli distribution.
Samples are logits of values in (0, 1). See [1] for more details.
Parameters:
      * temperature (Tensor) -- relaxation temperature
  * **probs** (*Number**, **Tensor*) -- the probability of
    sampling *1*

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

[1] The Concrete Distribution: A Continuous Relaxation of Discrete
   Random Variables (Maddison et al, 2017)
[2] Categorical Reparametrization with Gumbel-Softmax (Jang et al,
   2017)
arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)}
expand(batch_shape, _instance=None)
log_prob(value)