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 unittest
  import torch
  import gpytorch
  from gpytorch.models import ApproximateGP
  from gpytorch.test.model_test_case import VariationalModelTestCase
  from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
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