code: def forward(self, x):
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      return MultivariateNormal(mean_x, covar_x)
  
file_path: gpytorch/test/mlls/test_exact_marginal_log_likelihood.py
imports: import unittest
  import torch
  from gpytorch.constraints.constraints import GreaterThan
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means.constant_mean import ConstantMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.priors.torch_priors import GammaPrior
label_desc: Use image node_id 2 for calling the ExactGPModel obj's underlying member method code with example usage: obj.forward(x) and returns: MultivariateNormal