code: def forward(self, x: Tensor, **kwargs) -> MultivariateNormal:
      mean = self.mean_module(x)
      covar = self.covar_module(x)
      return MultivariateNormal(mean, covar)
  
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 2 for calling the _BaseExactGP obj's underlying member method code with example usage: obj.forward(x) and returns: MultivariateNormal