class_docstr: Computes a kernel based on the symmetrized KL divergence, assuming that two Gaussian
  distributions are inputted. Inputs are assumed to be `batch x N x 2d` tensors where `d` is the
  dimension of the distribution. The first `d` dimensions are the mean parameters of the
  `batch x N` distributions, while the second `d` dimensions are the log variances.
  
  Original citation is Moreno et al, '04
  (https://papers.nips.cc/paper/2351-a-kullback-leibler-divergence-based-kernel-for-svm-\
  classification-in-multimedia-applications.pdf) for the symmetrized KL divergence kernel between
  two Gaussian distributions.
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code: def __init__(self, **kwargs):
      distance_function = _symmetrized_kl
      super(GaussianSymmetrizedKLKernel, self).__init__(
          distance_function=distance_function, **kwargs
      )
  
imports: from .distributional_input_kernel import DistributionalInputKernel
label_desc: Use image node_id 1 to create a new GaussianSymmetrizedKLKernel object from inherited base classes: DistributionalInputKernel with example: obj = GaussianSymmetrizedKLKernel()