code: def test_grid_gp_mean_abs_error(self, num_dim=1, cuda=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
      grid_bounds = [(0, 1)] if num_dim == 1 else [(0, 1), (0, 2)]
      grid_size = 25
      grid = torch.zeros(grid_size, len(grid_bounds), device=device)
      for i in range(len(grid_bounds)):
          grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (
              grid_size - 2
          )
          grid[:, i] = torch.linspace(
              grid_bounds[i][0] - grid_diff,
              grid_bounds[i][1] + grid_diff,
              grid_size,
              device=device,
          )
  
      train_x, train_y, test_x, test_y = make_data(grid, cuda=cuda)
      likelihood = gpytorch.likelihoods.GaussianLikelihood()
      gp_model = GridGPRegressionModel(
          grid, train_x, train_y, likelihood
      )
      mll = gpytorch.mlls.ExactMarginalLogLikelihood(
          likelihood, gp_model
      )
  
      if cuda:
          gp_model.cuda()
          likelihood.cuda()
  
      # 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(True):
          for _ in range(20):
              optimizer.zero_grad()
              output = gp_model(train_x)
              loss = -mll(output, train_y)
              loss.backward()
              optimizer.n_iter += 1
              optimizer.step()
  
          for name, param in gp_model.named_parameters():
              self.assertTrue(param.grad is not None)
              self.assertGreater(param.grad.norm().item(), 0)
  
          # Test the model
          gp_model.eval()
          likelihood.eval()
          # Make sure we don't get GP input warnings for testing on training data
          warnings.simplefilter("ignore", GPInputWarning)
  
          train_preds = likelihood(gp_model(train_x)).mean
          mean_abs_error = torch.mean(torch.abs(train_y - train_preds))
  
      self.assertLess(mean_abs_error.squeeze().item(), 0.3)
  
imports: import math
  import os
  import random
  import unittest
  import warnings
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.utils.warnings import GPInputWarning
label_desc: Use image node_id 3 for calling the TestGridGPRegression obj's underlying member method code with example usage: obj.test_grid_gp_mean_abs_error(num_dim, cuda) without return types