icdf(value)
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
property stddev
support = Real()
property variance
LogNormal
class torch.distributions.log_normal.LogNormal(loc, scale, validate_args=None)
Bases: "TransformedDistribution"
Creates a log-normal distribution parameterized by "loc" and
   "scale" where:
  X ~ Normal(loc, scale)
  Y = exp(X) ~ LogNormal(loc, scale)

Example:
  >>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
  >>> m.sample()  # log-normal distributed with mean=0 and stddev=1
  tensor([ 0.1046])

Parameters:
      * loc (float or Tensor) -- mean of log of distribution
  * **scale** (*float** or **Tensor*) -- standard deviation of log
    of the distribution

arg_constraints: Dict[str, constraints.Constraint] = {'loc': Real(), 'scale': GreaterThan(lower_bound=0.0)}
entropy()
expand(batch_shape, _instance=None)
has_rsample = True