code: def test_ard(self):
      a = torch.tensor([[1, 2], [2, 4]], dtype=torch.float)
      b = torch.tensor([[1, 3], [0, 4]], dtype=torch.float)
      lengthscales = torch.tensor([1, 2], dtype=torch.float).view(1, 2)
  
      kernel = RBFKernel(ard_num_dims=2)
      kernel.initialize(lengthscale=lengthscales)
      kernel.eval()
  
      scaled_a = a.div(lengthscales)
      scaled_b = b.div(lengthscales)
      actual = (
          (scaled_a.unsqueeze(-2) - scaled_b.unsqueeze(-3))
          .pow(2)
          .sum(dim=-1)
          .mul_(-0.5)
          .exp()
      )
      res = kernel(a, b).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # Diag
      res = kernel(a, b).diagonal(dim1=-1, dim2=-2)
      actual = actual.diagonal(dim1=-1, dim2=-2)
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # batch_dims
      actual = scaled_a.transpose(-1, -2).unsqueeze(
          -1
      ) - scaled_b.transpose(-1, -2).unsqueeze(-2)
      actual = actual.pow(2).mul_(-0.5).exp()
      res = kernel(a, b, last_dim_is_batch=True).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-5)
  
      # batch_dims and diag
      res = kernel(a, b, last_dim_is_batch=True).diagonal(
          dim1=-1, dim2=-2
      )
      actual = actual.diagonal(dim1=-1, dim2=-2)
      self.assertLess(torch.norm(res - actual), 1e-5)
  
imports: import math
  import pickle
  import unittest
  import torch
  from gpytorch.kernels import RBFKernel
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 3 for calling the TestRBFKernel obj's underlying member method code with example usage: obj.test_ard() without return types