class_docstr: 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
  
  .. math::
  
      p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x))
  
  where :math:`\theta` denotes the natural parameters, :math:`t(x)` denotes the sufficient statistic,
  :math:`F(\theta)` is the log normalizer function for a given family and :math:`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 (courtesy of: Frank Nielsen and Richard Nock, Entropies and
      Cross-entropies of Exponential Families).
docstr: Abstract method for natural parameters. Returns a tuple of Tensors based
  on the distribution
docstr_tok: ["Abstract","method","for","natural","parameters",".","Returns","a","tuple","of","Tensors","based","on","the","distribution"]
code: def _natural_params(self):
      """
      Abstract method for natural parameters. Returns a tuple of Tensors based
      on the distribution
      """
      raise NotImplementedError
  
imports: import torch
  from torch.distributions.distribution import Distribution
label_desc: Use image node_id 1 for calling the ExponentialFamily obj's underlying member method code with example usage: obj._natural_params() without return types