original_docstring: r"""
              Computes a predictive distribution :math:`p(y^* | \mathbf x^*)` given either a posterior
              distribution :math:`p(\mathbf f | \mathcal D, \mathbf x)` or a
              prior distribution :math:`p(\mathbf f|\mathbf x)` as input.
  
              With both exact inference and variational inference, the form of
              :math:`p(\mathbf f|\mathcal D, \mathbf x)` or :math:`p(\mathbf f|
              \mathbf x)` should usually be Gaussian. As a result, :attr:`function_dist`
              should usually be a :obj:`~gpytorch.distributions.MultivariateNormal` specified by the mean and
              (co)variance of :math:`p(\mathbf f|...)`.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution for :math:`f(x)`.
              :param args: Additional args (passed to the foward function).
              :param kwargs: Additional kwargs (passed to the foward function).
              :return: The marginal distribution, or samples from it.
              :rtype: ~gpytorch.distributions.Distribution
              
docstring: 
  
  With both exact inference and variational inference, the form of
docstring_tokens: With both exact inference and variational inference the form of
code: def marginal(self, function_dist, *args, **kwargs):
              r"""
              Computes a predictive distribution :math:`p(y^* | \mathbf x^*)` given either a posterior
              distribution :math:`p(\mathbf f | \mathcal D, \mathbf x)` or a
              prior distribution :math:`p(\mathbf f|\mathbf x)` as input.
  
              With both exact inference and variational inference, the form of
              :math:`p(\mathbf f|\mathcal D, \mathbf x)` or :math:`p(\mathbf f|
              \mathbf x)` should usually be Gaussian. As a result, :attr:`function_dist`
              should usually be a :obj:`~gpytorch.distributions.MultivariateNormal` specified by the mean and
              (co)variance of :math:`p(\mathbf f|...)`.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution for :math:`f(x)`.
              :param args: Additional args (passed to the foward function).
              :param kwargs: Additional kwargs (passed to the foward function).
              :return: The marginal distribution, or samples from it.
              :rtype: ~gpytorch.distributions.Distribution
              """
              return super().marginal(function_dist, *args, **kwargs)
code_tokens: def marginal ( self , function_dist , * args , ** kwargs ) : return super ( ) . marginal ( function_dist , * args , ** kwargs )
short_docstring: r"""
  Computes a predictive distribution :math:`p(y^* | \mathbf x^*)` given either a posterior
  distribution :math:`p(\mathbf f | \mathcal D, \mathbf x)` or a
  prior distribution :math:`p(\mathbf f|\mathbf x)` as input.
short_docstring_tokens: r " " " Computes a predictive distribution : math : ` p ( y^ * | \ mathbf x^ * ) ` given either a posterior distribution : math : ` p ( \ mathbf f | \ mathcal D \ mathbf x ) ` or a prior distribution : math : ` p ( \ mathbf f| \ mathbf x ) ` as input .
comment: r"""
              Computes a predictive distribution :math:`p(y^* | \mathbf x^*)` given either a posterior
              distribution :math:`p(\mathbf f | \mathcal D, \mathbf x)` or a
              prior distribution :math:`p(\mathbf f|\mathbf x)` as input.
  
              With both exact inference and variational inference, the form of
              :math:`p(\mathbf f|\mathcal D, \mathbf x)` or :math:`p(\mathbf f|
              \mathbf x)` should usually be Gaussian. As a result, :attr:`function_dist`
              should usually be a :obj:`~gpytorch.distributions.MultivariateNormal` specified by the mean and
              (co)variance of :math:`p(\mathbf f|...)`.
  
              :param ~gpytorch.distributions.MultivariateNormal function_dist: Distribution for :math:`f(x)`.
              :param args: Additional args (passed to the foward function).
              :param kwargs: Additional kwargs (passed to the foward function).
              :return: The marginal distribution, or samples from it.
              :rtype: ~gpytorch.distributions.Distribution
              """