class_docstr: Computes a covariance matrix over __Gaussian__ distributions via exponentiating the
  distance function between probability distributions.
  .. math::
  
      \begin{equation*}
          k(p(x), p(x')) = \exp\{-a d(p(x), p(x'))\})
      \end{equation*}
  
  where :math:`a` is the lengthscale.
  
  Args:
      distance_function (function) distance function between distributional inputs.
code: def forward(self, x1, x2, diag=False, *args, **kwargs):
      negative_covar_func = -self.distance_function(x1, x2)
      res = negative_covar_func.div(self.lengthscale).exp()
  
      if not diag:
          return res
      else:
          if torch.is_tensor(res):
              return res.diagonal(dim1=-1, dim2=-2)
          else:
              return res.diagonal(dim1=-1, dim2=-2)
  
imports: from typing import Callable
  import torch
  from .kernel import Kernel
label_desc: Use image node_id 2 for calling the DistributionalInputKernel obj's underlying member method code with example usage: obj.forward(x1, x2, diag) and returns: res, res, res