original_docstring: r"""
              (For Pyro integration only).
  
              Part of the model function for the likelihood.
              It should return the
              This should be re-defined if the likelihood contains any latent variables that need to be infered.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution of latent function
                  :math:`p(\mathbf f)`.
              :param torch.Tensor target: Observed :math:`\mathbf y`.
              :param args: Additional args (for :meth:`~forward`).
              :param kwargs: Additional kwargs (for :meth:`~forward`).
              
docstring: r"""
  (For Pyro integration only).
  
  Part of the model function for the likelihood.
  It should return the
  This should be re-defined if the likelihood contains any latent variables that need to be infered.
docstring_tokens: r " " " ( For Pyro integration only ) . Part of the model function for the likelihood . It should return the This should be re - defined if the likelihood contains any latent variables that need to be infered .
code: def pyro_model(self, function_dist, target, *args, **kwargs):
              r"""
              (For Pyro integration only).
  
              Part of the model function for the likelihood.
              It should return the
              This should be re-defined if the likelihood contains any latent variables that need to be infered.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution of latent function
                  :math:`p(\mathbf f)`.
              :param torch.Tensor target: Observed :math:`\mathbf y`.
              :param args: Additional args (for :meth:`~forward`).
              :param kwargs: Additional kwargs (for :meth:`~forward`).
              """
              with pyro.plate(self.name_prefix + ".data_plate", dim=-1):
                  function_samples = pyro.sample(self.name_prefix + ".f", function_dist)
                  output_dist = self(function_samples, *args, **kwargs)
                  return self.sample_target(output_dist, target)
code_tokens: def pyro_model ( self , function_dist , target , * args , ** kwargs ) : with pyro . plate ( self . name_prefix + ".data_plate" , dim = - 1 ) : function_samples = pyro . sample ( self . name_prefix + ".f" , function_dist ) output_dist = self ( function_samples , * args , ** kwargs ) return self . sample_target ( output_dist , target )
short_docstring: r"""
  (For Pyro integration only).
short_docstring_tokens: r " " " ( For Pyro integration only ) .
comment: r"""
              (For Pyro integration only).
  
              Part of the model function for the likelihood.
              It should return the
              This should be re-defined if the likelihood contains any latent variables that need to be infered.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution of latent function
                  :math:`p(\mathbf f)`.
              :param torch.Tensor target: Observed :math:`\mathbf y`.
              :param args: Additional args (for :meth:`~forward`).
              :param kwargs: Additional kwargs (for :meth:`~forward`).
              """