Bases: "ExponentialFamily"
Creates a Exponential distribution parameterized by "rate".
Example:
  >>> m = Exponential(torch.tensor([1.0]))
  >>> m.sample()  # Exponential distributed with rate=1
  tensor([ 0.1046])

Parameters:
      rate (float or Tensor) -- rate = 1 / scale of the
      distribution
arg_constraints = {'rate': GreaterThan(lower_bound=0.0)}
cdf(value)
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
icdf(value)
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
property stddev
support = GreaterThanEq(lower_bound=0.0)
property variance
FisherSnedecor
class torch.distributions.fishersnedecor.FisherSnedecor(df1, df2, validate_args=None)
Bases: "Distribution"
Creates a Fisher-Snedecor distribution parameterized by "df1" and
   "df2".
Example:
  >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
