#Ki LSSKJr SSKrSSKJr SSKJr S/r"SS\5rg))UnionN)Tensor) DistributionExponentialFamilyc^\rSrSrSr\S\\S44Sj5rSr \S\ 4Sj5r Sr S r g ) r a 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). return.c[e)z^ Abstract method for natural parameters. Returns a tuple of Tensors based on the distribution NotImplementedErrorselfs `/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/torch/distributions/exp_family.py_natural_params!ExponentialFamily._natural_params! "!c[e)zk Abstract method for log normalizer function. Returns a log normalizer based on the distribution and input r )rnatural_paramss r_log_normalizer!ExponentialFamily._log_normalizer)s "!rc[e)zX Abstract method for expected carrier measure, which is required for computing entropy. r r s r_mean_carrier_measure'ExponentialFamily._mean_carrier_measure0rrcUR*nURVs/sH o"R5R5PM" nnUR"U6n[ R RUR5USS9nX- n[X55H7upgXU-RURS-5RS5-nM9 U$s snf)zO Method to compute the entropy using Bregman divergence of the log normalizer. T) create_graph)r) rrdetachrequires_grad_rtorchautogradgradsumzipreshape _batch_shape)rresultpnparams lg_normal gradientsnpgs rentropyExponentialFamily.entropy8s)-(B(B'B8<8L8LM8L188:,,.8LM(('2 NN'' t'T ,EB Av&&t'8'85'@AEEbI IF- Ns'C N)__name__ __module__ __qualname____firstlineno____doc__propertytuplerrrfloatrr.__static_attributes__r0rrrr sP("vs{!3""""u"" r)typingrr r torch.distributions.distributionr__all__rr0rrr=s' 9  7 7r