code: def test_computes_sum_of_three_radial_basis_function(self):
      a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
      b = torch.tensor([0, 2], dtype=torch.float).view(2, 1)
      lengthscale = 2
  
      kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel_3 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel = (kernel_1 + kernel_2) + kernel_3
      self.assertEqual(len(kernel.kernels), 3)
      for sub_kernel in kernel.kernels:
          self.assertIsInstance(sub_kernel, RBFKernel)
  
      actual = (
          torch.tensor([[16, 4], [4, 0], [64, 36]], dtype=torch.float)
          .mul_(-0.5)
          .div_(lengthscale**2)
          .exp()
          * 3
      )
  
      kernel.eval()
      res = kernel(a, b).to_dense()
      self.assertLess(torch.norm(res - actual), 2e-5)
  
      kernel_1 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel_2 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel_3 = RBFKernel().initialize(lengthscale=lengthscale)
      kernel = kernel_1 + (kernel_2 + kernel_3)
      self.assertEqual(len(kernel.kernels), 3)
      for sub_kernel in kernel.kernels:
          self.assertIsInstance(sub_kernel, RBFKernel)
  
      kernel.eval()
      res = kernel(a, b).to_dense()
      self.assertLess(torch.norm(res - actual), 2e-5)
  
imports: import math
  import unittest
  import linear_operator
  import torch
  import gpytorch
  from gpytorch.kernels import LinearKernel, MaternKernel, RBFKernel, RFFKernel
label_desc: Use image node_id 11 for calling the TestAdditiveAndProductKernel obj's underlying member method code with example usage: obj.test_computes_sum_of_three_radial_basis_function() without return types