code: def forward(self, x):
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      latent_pred = gpytorch.distributions.MultivariateNormal(
          mean_x, covar_x
      )
      return latent_pred
  
imports: import math
  import unittest
  import warnings
  from pathlib import Path
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.utils.warnings import OldVersionWarning
label_desc: Use image node_id 2 for calling the SVGPRegressionModel obj's underlying member method code with example usage: obj.forward(x) and returns: latent_pred