class_docstr: Base class for Priors in GPyTorch.
  In GPyTorch, a parameter can be assigned a prior by passing it as the `prior` argument to
  :func:`~gpytorch.module.register_parameter`. GPyTorch performs internal bookkeeping of priors,
  and for each parameter with a registered prior includes the log probability of the parameter under its
  respective prior in computing the Marginal Log-Likelihood.
docstr: :return: log-probability of the parameter value under the prior
  :rtype: torch.Tensor
code: def log_prob(self, x):
      r"""
      :return: log-probability of the parameter value under the prior
      :rtype: torch.Tensor
      """
      return super(Prior, self).log_prob(self.transform(x))
  
file_path: gpytorch/gpytorch/priors/prior.py
imports: from abc import ABC
  from torch.nn import Module
  from ..distributions import Distribution
label_desc: Use image node_id 2 for calling the Prior obj's underlying member method code with example usage: obj.log_prob(x) and returns: super