code: def forward(self, x):
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      latent_pred = MultivariateNormal(mean_x, covar_x)
      return latent_pred
  
imports: import os
  import random
  import unittest
  from math import exp
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
label_desc: Use image node_id 2 for calling the GPClassificationModel obj's underlying member method code with example usage: obj.forward(x) and returns: latent_pred