code: def forward(self, x):
      # The forward function should be written as if we were dealing with each output
      # dimension in batch
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
  
file_path: gpytorch/test/examples/test_lmc_svgp_regression.py
imports: import math
  import os
  import random
  import unittest
  import torch
  import gpytorch
  from gpytorch.likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
label_desc: Use image node_id 2 for calling the LMCModel obj's underlying member method code with example usage: obj.forward(x) and returns: gpytorch