new_contents: #!/usr/bin/env python3
  
  import unittest
  
  import torch
  
  from gpytorch.kernels import GridInterpolationKernel, RBFKernel
  from gpytorch.lazy import InterpolatedLazyTensor
  
  
  class TestGridInterpolationKernel(unittest.TestCase):
      def test_standard(self):
          base_kernel = RBFKernel()
          kernel = GridInterpolationKernel(base_kernel, num_dims=2, grid_size=128, grid_bounds=[(-1.2, 1.2)] * 2)
  
          xs = torch.randn(5, 2).clamp(-1, 1)
          interp_covar = kernel(xs, xs).evaluate_kernel()
          self.assertIsInstance(interp_covar, InterpolatedLazyTensor)
  
          xs = torch.randn(5, 2).clamp(-1, 1)
          grid_eval = kernel(xs, xs).evaluate()
          actual_eval = base_kernel(xs, xs).evaluate()
          self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
  
          xs = torch.randn(3, 5, 2).clamp(-1, 1)
          grid_eval = kernel(xs, xs).evaluate()
          actual_eval = base_kernel(xs, xs).evaluate()
          self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
  
      def test_batch_base_kernel(self):
          base_kernel = RBFKernel(batch_shape=torch.Size([3]))
          kernel = GridInterpolationKernel(base_kernel, num_dims=2, grid_size=128, grid_bounds=[(-1.2, 1.2)] * 2)
  
          xs = torch.randn(5, 2).clamp(-1, 1)
          grid_eval = kernel(xs, xs).evaluate()
          actual_eval = base_kernel(xs, xs).evaluate()
          self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
  
          xs = torch.randn(3, 5, 2).clamp(-1, 1)
          grid_eval = kernel(xs, xs).evaluate()
          actual_eval = base_kernel(xs, xs).evaluate()
          self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
  
          xs = torch.randn(4, 3, 5, 2).clamp(-1, 1)
          grid_eval = kernel(xs, xs).evaluate()
          actual_eval = base_kernel(xs, xs).evaluate()
          self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
  
  
  if __name__ == "__main__":
      unittest.main()
  
subject: Add grid interpolation tests