code: def test_keops_gp_mean_abs_error(self):
      try:
          import pykeops  # noqa
      except ImportError:
          return
  
      likelihood = GaussianLikelihood()
      gp_model = KeOpsGPModel(train_x, train_y, likelihood)
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(
          likelihood, gp_model
      )
  
      # Optimize the model
      gp_model.train()
      likelihood.train()
      optimizer = optim.Adam(list(gp_model.parameters()), lr=0.01)
      optimizer.n_iter = 0
  
      with gpytorch.settings.max_cholesky_size(
          0
      ):  # Ensure that we're using KeOps
          for i in range(300):
              optimizer.zero_grad()
              output = gp_model(train_x)
              loss = -mll(output, train_y)
              loss.backward()
              optimizer.step()
  
              if i == 0:
                  for param in gp_model.parameters():
                      self.assertTrue(param.grad is not None)
  
          # Test the model
          with torch.no_grad():
              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(), 0.1)
  
file_path: gpytorch/test/examples/test_keops_gp_regression.py
imports: import unittest
  from math import pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import ScaleKernel
  from gpytorch.kernels.keops import RBFKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 for calling the TestKeOpsGPRegression obj's underlying member method code with example usage: obj.test_keops_gp_mean_abs_error() without return types