Bases: "ExponentialFamily"
Creates a Poisson distribution parameterized by "rate", the rate
   parameter.
Samples are nonnegative integers, with a pmf given by
  \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}

Example:
  >>> m = Poisson(torch.tensor([4]))
  >>> m.sample()
  tensor([ 3.])

Parameters:
      rate (Number, Tensor) -- the rate parameter
arg_constraints = {'rate': GreaterThanEq(lower_bound=0.0)}
expand(batch_shape, _instance=None)
log_prob(value)
property mean
property mode
sample(sample_shape=torch.Size([]))
support = IntegerGreaterThan(lower_bound=0)
property variance
RelaxedBernoulli
class torch.distributions.relaxed_bernoulli.RelaxedBernoulli(temperature, probs=None, logits=None, validate_args=None)
Bases: "TransformedDistribution"
Creates a RelaxedBernoulli distribution, parametrized by
   "temperature", and either "probs" or "logits" (but not both). This