class_docstr: Abstract base class for all Variational Strategies.
docstr: The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
  inducing point function values. Specifically, forward defines how to transform a variational distribution
  over the inducing point values, :math:`q(u)`, in to a variational distribution over the function values at
  specified locations x, :math:`q(f|x)`, by integrating :math:`\int p(f|x, u)q(u)du`
  
  :param x: Locations :math:`\mathbf X` to get the
      variational posterior of the function values at.
  :param inducing_points: Locations :math:`\mathbf Z` of the inducing points
  :param inducing_values: Samples of the inducing function values :math:`\mathbf u`
      (or the mean of the distribution :math:`q(\mathbf u)` if q is a Gaussian.
  :param variational_inducing_covar: If
      the distribuiton :math:`q(\mathbf u)` is
      Gaussian, then this variable is the covariance matrix of that Gaussian.
      Otherwise, it will be None.
  
  :rtype: :obj:`~gpytorch.distributions.MultivariateNormal`
  :return: The distribution :math:`q( \mathbf f(\mathbf X))`
code: def forward(
      self,
      x: Tensor,
      inducing_points: Tensor,
      inducing_values: Tensor,
      variational_inducing_covar: Optional[LinearOperator] = None,
      **kwargs,
  ) -> MultivariateNormal:
      r"""
      The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
      inducing point function values. Specifically, forward defines how to transform a variational distribution
      over the inducing point values, :math:`q(u)`, in to a variational distribution over the function values at
      specified locations x, :math:`q(f|x)`, by integrating :math:`\int p(f|x, u)q(u)du`
  
      :param x: Locations :math:`\mathbf X` to get the
          variational posterior of the function values at.
      :param inducing_points: Locations :math:`\mathbf Z` of the inducing points
      :param inducing_values: Samples of the inducing function values :math:`\mathbf u`
          (or the mean of the distribution :math:`q(\mathbf u)` if q is a Gaussian.
      :param variational_inducing_covar: If
          the distribuiton :math:`q(\mathbf u)` is
          Gaussian, then this variable is the covariance matrix of that Gaussian.
          Otherwise, it will be None.
  
      :rtype: :obj:`~gpytorch.distributions.MultivariateNormal`
      :return: The distribution :math:`q( \mathbf f(\mathbf X))`
      """
      raise NotImplementedError
  
imports: import functools
  from abc import ABC, abstractproperty
  from copy import deepcopy
  from typing import Optional, Tuple, Union
  import torch
  from linear_operator.operators import LinearOperator
  from torch import Tensor
  from ..None import settings
  from ..distributions import Delta, Distribution, MultivariateNormal
  from ..kernels import Kernel
  from ..likelihoods import GaussianLikelihood
  from ..means import Mean
  from ..models import ApproximateGP, ExactGP
  from ..models.exact_prediction_strategies import DefaultPredictionStrategy
  from ..module import Module
  from ..utils.memoize import add_to_cache, cached, clear_cache_hook
  from .None import _VariationalDistribution
label_desc: Use image node_id 8 for calling the _VariationalStrategy obj's underlying member method code with example usage: obj.forward(x, inducing_points, inducing_values, variational_inducing_covar) without return types