new_contents: import gpytorch
  import torch
  import math
  import unittest
  import os
  import random
  from torch import optim
  
  
  def make_data(grid, cuda=False):
      train_x = gpytorch.utils.grid.create_data_from_grid(grid)
      train_y = torch.sin((train_x[:, 0] + train_x[:, 1]) * (2 * math.pi)) + torch.randn_like(train_x[:, 0]).mul(0.01)
      n = 20
      test_x = torch.zeros(int(pow(n, 2)), 2)
      for i in range(n):
          for j in range(n):
              test_x[i * n + j][0] = float(i) / (n - 1)
              test_x[i * n + j][1] = float(j) / (n - 1)
      test_y = torch.sin(((test_x[:, 0] + test_x[:, 1]) * (2 * math.pi)))
      if cuda:
          train_x = train_x.cuda()
          train_y = train_y.cuda()
          test_x = test_x.cuda()
          test_y = test_y.cuda()
      return train_x, train_y, test_x, test_y
  
  
  class GridGPRegressionModel(gpytorch.models.ExactGP):
      def __init__(self, grid, train_x, train_y, likelihood):
          super(GridGPRegressionModel, self).__init__(train_x, train_y, likelihood)
          self.mean_module = gpytorch.means.ConstantMean()
          self.covar_module = gpytorch.kernels.GridKernel(gpytorch.kernels.RBFKernel(), grid=grid)
  
      def forward(self, x):
          mean_x = self.mean_module(x)
          covar_x = self.covar_module(x)
          return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
  
  
  class TestGridGPRegression(unittest.TestCase):
      def setUp(self):
          if os.getenv("UNLOCK_SEED") is None or os.getenv("UNLOCK_SEED").lower() == "false":
              self.rng_state = torch.get_rng_state()
              torch.manual_seed(0)
              if torch.cuda.is_available():
                  torch.cuda.manual_seed_all(0)
              random.seed(0)
  
      def tearDown(self):
          if hasattr(self, "rng_state"):
              torch.set_rng_state(self.rng_state)
  
      def test_grid_gp_mean_abs_error(self):
          grid_bounds = [(0, 1), (0, 2)]
          grid_size = 25
          grid = torch.zeros(grid_size, len(grid_bounds))
          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)
  
          train_x, train_y, test_x, test_y = make_data(grid)
          likelihood = gpytorch.likelihoods.GaussianLikelihood()
          gp_model = GridGPRegressionModel(grid, 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()) + list(likelihood.parameters()), lr=0.1)
          optimizer.n_iter = 0
          with gpytorch.settings.debug(False):
              for _ in range(25):
                  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)
              for param in likelihood.parameters():
                  self.assertTrue(param.grad is not None)
                  self.assertGreater(param.grad.norm().item(), 0)
  
              # Test the model
              gp_model.eval()
              likelihood.eval()
  
              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(), 0.1)
  
  
  if __name__ == "__main__":
      unittest.main()
  
subject: add grid GP unit test