code: def __init__(self, train_x, train_y, likelihood):
      super().__init__(train_x, train_y, likelihood)
      self.mean_module = gpytorch.means.ConstantMean()
      covar_a = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.RBFKernel()
      )
      covar_b = gpytorch.kernels.ScaleKernel(
          gpytorch.kernels.MaternKernel(nu=0.5)
      )
      covar_c = (
          gpytorch.kernels.LinearKernel()
      )  # this one is important because its covariance matrix can be lazy
      self.covar_module = covar_a + covar_b + covar_c
  
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 1 to create a new SumExactGPModel object from inherited base classes: ExactGP with example: obj = SumExactGPModel(train_x, train_y, likelihood)