class_docstr: ExponentialFamily inherits from Distribution. ExponentialFamily is a base
  class for distributions whose density function has the form:
  p_F(x;\theta) = exp(
      <t(x), \theta> -
      F(\theta) +
      k(x)
  ) where
  t(x): sufficient statistics
  \theta: natural parameters
  F(\theta): log_normalizer
  k(x): carrier measure
docstr: Return the entropy of a distribution.
  The entropy of distributions in exponential families
  could be computed by:
  H(P) = F(\theta) - <\theta, F(\theta)'> - E_p[k(x)]
docstr_tok: ["Return","the","entropy","of","a","distribution",".","The","entropy","of","distributions","in","exponential","families","could","be","computed","by",":","H","(","P",")","=","F","(","\\theta",")","-","<","\\theta",",","F","(","\\theta",")","'",">","-","E_p","[","k","(","x",")","]"]
code: def entropy(self):
      r"""
      Return the entropy of a distribution.
      The entropy of distributions in exponential families
      could be computed by:
      H(P) = F(\theta) - <\theta, F(\theta)'> - E_p[k(x)]
      """
      raise NotImplementedError
  
imports: from .distribution import Distribution
label_desc: Use image node_id 4 for calling the ExponentialFamily obj's underlying member method code with example usage: obj.entropy() without return types