tensor([ 0.1046])
Parameters:
      scale (float or Tensor) -- scale of the full Normal
      distribution
arg_constraints: Dict[str, constraints.Constraint] = {'scale': GreaterThan(lower_bound=0.0)}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(prob)
log_prob(value)
property mean
property mode
property scale
support = GreaterThanEq(lower_bound=0.0)
property variance
Independent
class torch.distributions.independent.Independent(base_distribution, reinterpreted_batch_ndims, validate_args=None)
Bases: "Distribution"
Reinterprets some of the batch dims of a distribution as event
   dims.
This is mainly useful for changing the shape of the result of
   "log_prob()". For example to create a diagonal Normal distribution
   with the same shape as a Multivariate Normal distribution (so they
   are interchangeable), you can: