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 __init__(
      self,
      distance_function: Callable,
      **kwargs,
  ):
      super(DistributionalInputKernel, self).__init__(**kwargs)
      if distance_function is None:
          raise NotImplementedError(
              "DistributionalInputKernel requires a distance function."
          )
  
      self.distance_function = distance_function
  
imports: from typing import Callable
  import torch
  from .kernel import Kernel
label_desc: Use image node_id 1 to create a new DistributionalInputKernel object from inherited base classes: Kernel with example: obj = DistributionalInputKernel(distance_function)