code: def forward(self, x):
      mean_x = self.mean_module(x)
      covar_x = self.covar_module(x)
      return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch import settings
  from gpytorch.kernels import GridInterpolationKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import ExactMarginalLogLikelihood
  from gpytorch.models import ExactGP
  from gpytorch.models.exact_prediction_strategies import InterpolatedPredictionStrategy
  from gpytorch.test.model_test_case import BaseModelTestCase
label_desc: Use image node_id 2 for calling the InterpolatedExactGPModel obj's underlying member method code with example usage: obj.forward(x) and returns: gpytorch