ExponentialFamily
class torch.distributions.exp_family.ExponentialFamily(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None)
Bases: "Distribution"
ExponentialFamily is the abstract base class for probability
   distributions belonging to an exponential family, whose probability
   mass/density function has the form is defined below
  p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta)
  + k(x))

where \theta denotes the natural parameters, t(x) denotes the
   sufficient statistic, F(\theta) is the log normalizer function for
   a given family and k(x) is the carrier measure.
Note:
 This class is an intermediary between the *Distribution* class
 and distributions which belong to an exponential family mainly to
 check the correctness of the *.entropy()* and analytic KL
 divergence methods. We use this class to compute the entropy and
 KL divergence using the AD framework and Bregman divergences
