code: def test_computes_sum_three_radial_basis_function_gradient(self):
      softplus = torch.nn.functional.softplus
      a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
      b = torch.tensor([0, 2, 2], dtype=torch.float).view(3, 1)
      lengthscale = 2
  
      param = math.log(math.exp(lengthscale) - 1) * torch.ones(3, 3)
      param.requires_grad_()
      diffs = a.expand(3, 3) - b.expand(3, 3).transpose(0, 1)
      actual_output = (-0.5 * (diffs / softplus(param)) ** 2).exp()
      actual_output.backward(torch.eye(3))
      actual_param_grad = param.grad.sum() * 3
  
      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
      kernel.eval()
  
      output = kernel(a, b).to_dense()
      output.backward(gradient=torch.eye(3))
      res = (
          kernel.kernels[0].raw_lengthscale.grad
          + kernel.kernels[1].raw_lengthscale.grad
          + kernel.kernels[2].raw_lengthscale.grad
      )
      self.assertLess(torch.norm(res - actual_param_grad), 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 13 for calling the TestAdditiveAndProductKernel obj's underlying member method code with example usage: obj.test_computes_sum_three_radial_basis_function_gradient() without return types