code: def test_dkl_gp_mean_abs_error(self):
      train_x, train_y, test_x, test_y = make_data()
      likelihood = GaussianLikelihood()
      gp_model = GPRegressionModel(train_x, train_y, likelihood)
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(
          likelihood, gp_model
      )
  
      # Optimize the model
      gp_model.train()
      likelihood.train()
  
      optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
      optimizer.n_iter = 0
      with gpytorch.settings.debug(False):
          for _ in range(30):
              optimizer.zero_grad()
              output = gp_model(train_x)
              loss = -mll(output, train_y)
              loss.backward()
              optimizer.n_iter += 1
              optimizer.step()
  
          for param in gp_model.parameters():
              self.assertTrue(param.grad is not None)
              self.assertGreater(param.grad.norm().item(), 0)
  
          # Test the model
          gp_model.eval()
          likelihood.eval()
  
          test_preds = likelihood(gp_model(test_x)).mean
          mean_abs_error = torch.mean(torch.abs(test_y - test_preds))
  
          self.assertLess(mean_abs_error.squeeze().item(), 0.15)
  
imports: import os
  import random
  import unittest
  from math import exp, pi
  import torch
  from torch import nn, optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import GridInterpolationKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
label_desc: Use image node_id 3 for calling the TestDKLRegression obj's underlying member method code with example usage: obj.test_dkl_gp_mean_abs_error() without return types