code: def test_ard(self):
      base_k = RBFKernel(ard_num_dims=3)
      base_k.initialize(lengthscale=[1.0, 2.0, 3.0])
      AddK = NewtonGirardAdditiveKernel(base_k, 3, max_degree=1)
  
      testvals = torch.tensor([[1, 2, 3], [7, 5, 2]], dtype=torch.float)
      add_k_val = AddK(testvals, testvals).to_dense()
  
      ks = []
      for i in range(3):
          k = RBFKernel(active_dims=i)
          k.initialize(lengthscale=i + 1)
          ks.append(k)
      manual_k = ScaleKernel(AdditiveKernel(*ks))
      manual_k.initialize(outputscale=1.0)
      manual_add_k_val = manual_k(testvals, testvals).to_dense()
  
      # np.testing.assert_allclose(add_k_val.detach().numpy(), manual_add_k_val.detach().numpy(), atol=1e-5)
      self.assertTrue(
          torch.allclose(add_k_val, manual_add_k_val, atol=1e-5)
      )
  
file_path: gpytorch/test/kernels/test_newton_girard_additive_kernel.py
imports: from unittest import TestCase
  import torch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import AdditiveKernel, NewtonGirardAdditiveKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.mlls import ExactMarginalLogLikelihood
  from gpytorch.models import ExactGP
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 7 for calling the TestNewtonGirardAdditiveKernel obj's underlying member method code with example usage: obj.test_ard() without return types