log_prob(value)
property logits
property param_shape
property probs
rsample(sample_shape=torch.Size([]))
support = Real()
RelaxedOneHotCategorical
class torch.distributions.relaxed_categorical.RelaxedOneHotCategorical(temperature, probs=None, logits=None, validate_args=None)
Bases: "TransformedDistribution"
Creates a RelaxedOneHotCategorical distribution parametrized by
   "temperature", and either "probs" or "logits". This is a relaxed
   version of the "OneHotCategorical" distribution, so its samples are
   on simplex, and are reparametrizable.
Example:
  >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
  ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
  >>> m.sample()
  tensor([ 0.1294,  0.2324,  0.3859,  0.2523])

Parameters:
      * temperature (Tensor) -- relaxation temperature
  * **probs** (*Tensor*) -- event probabilities
