code: def test_computes_product_of_radial_basis_function_diag(self):
      a = torch.tensor([4, 2, 8], dtype=torch.float).view(3, 1)
      b = torch.tensor([0, 2, 2], dtype=torch.float).view(3, 1)
  
      actual = torch.tensor([2.4788e-03, 1.000, 1.3710e-06])
  
      lengthscale = 2.0
  
      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()
  
      kernel.eval()
      res = kernel(a, b, diag=True)
      self.assertLess(torch.norm(res - actual), 1e-3)
  
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 6 for calling the TestAdditiveAndProductKernel obj's underlying member method code with example usage: obj.test_computes_product_of_radial_basis_function_diag() without return types