code: def test_kernel(self, cuda=False):
      a = torch.tensor([[[1, 2], [2, 4]]], dtype=torch.float)
      b = torch.tensor([[[1, 3], [0, 4]]], dtype=torch.float)
  
      actual = torch.tensor(
          [
              [
                  [
                      3.5321289e-01,
                      0.0000000e00,
                      -7.3516625e-01,
                      -7.3516631e-01,
                      7.9498571e-01,
                      5.4977159e-03,
                      1.1442775e-02,
                      -2.2885550e-02,
                      1.2373861e-02,
                      8.3823770e-02,
                  ],
                  [
                      -0.0000000e00,
                      7.3516631e-01,
                      0.0000000e00,
                      0.0000000e00,
                      -0.0000000e00,
                      -1.1442775e-02,
                      -1.2373861e-02,
                      4.7633272e-02,
                      2.1878703e-02,
                      -1.7446819e-01,
                  ],
                  [
                      7.3516625e-01,
                      0.0000000e00,
                      -7.9498571e-01,
                      -1.5301522e00,
                      -1.4056460e00,
                      2.2885550e-02,
                      4.7633272e-02,
                      -8.3823770e-02,
                      5.1509142e-02,
                      2.5366980e-01,
                  ],
                  [
                      -7.3516631e-01,
                      -0.0000000e00,
                      1.5301522e00,
                      4.5904574e00,
                      -1.6546586e00,
                      1.2373861e-02,
                      -2.1878703e-02,
                      -5.1509142e-02,
                      -1.2280136e-01,
                      1.8866448e-01,
                  ],
                  [
                      7.9498571e-01,
                      0.0000000e00,
                      1.4056460e00,
                      -1.6546586e00,
                      -7.8896437e00,
                      8.3823770e-02,
                      1.7446819e-01,
                      -2.5366980e-01,
                      1.8866447e-01,
                      5.3255635e-01,
                  ],
                  [
                      1.2475928e-01,
                      2.5967008e-01,
                      2.5967011e-01,
                      2.8079915e-01,
                      2.8079927e-01,
                      1.5564885e-02,
                      6.4792536e-02,
                      0.0000000e00,
                      2.3731807e-01,
                      -3.2396268e-02,
                  ],
                  [
                      -2.5967008e-01,
                      -2.8079915e-01,
                      -5.4046929e-01,
                      4.9649185e-01,
                      -5.8444691e-01,
                      -6.4792536e-02,
                      -2.3731807e-01,
                      0.0000000e00,
                      -7.1817851e-01,
                      1.3485716e-01,
                  ],
                  [
                      -2.5967011e-01,
                      -5.4046929e-01,
                      -2.8079927e-01,
                      -5.8444673e-01,
                      4.9649167e-01,
                      -0.0000000e00,
                      0.0000000e00,
                      3.2396268e-02,
                      0.0000000e00,
                      0.0000000e00,
                  ],
                  [
                      2.8079915e-01,
                      -4.9649185e-01,
                      5.8444673e-01,
                      -2.7867227e00,
                      6.3200271e-01,
                      2.3731807e-01,
                      7.1817851e-01,
                      0.0000000e00,
                      1.5077497e00,
                      -4.9394643e-01,
                  ],
                  [
                      2.8079927e-01,
                      5.8444691e-01,
                      -4.9649167e-01,
                      6.3200271e-01,
                      -2.7867231e00,
                      -3.2396268e-02,
                      -1.3485716e-01,
                      -0.0000000e00,
                      -4.9394643e-01,
                      2.0228577e-01,
                  ],
              ]
          ]
      )
  
      kernel = RBFKernelGradGrad()
  
      if cuda:
          a = a.cuda()
          b = b.cuda()
          actual = actual.cuda()
          kernel = kernel.cuda()
  
      res = kernel(a, b).to_dense()
  
      self.assertLess(torch.norm(res - actual), 1e-5)
  
file_path: gpytorch/test/kernels/test_rbf_kernel_gradgrad.py
imports: import unittest
  import torch
  from gpytorch.kernels import RBFKernelGradGrad
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 3 for calling the TestRBFKernelGradGrad obj's underlying member method code with example usage: obj.test_kernel(cuda) without return types