code: def test_kernel_output(self):
      train_x = torch.randn(1000, 3)
      train_y = torch.randn(1000)
      model = _TestModel(train_x, train_y)
  
      # Make sure that the prior kernel is the correct type
      model.train()
      output = model(train_x).lazy_covariance_matrix.evaluate_kernel()
      self.assertIsInstance(
          output, linear_operator.operators.SumLinearOperator
      )
  
      # Make sure that the prior predictive kernel is the correct type
      model.train()
      output = model.likelihood(
          model(train_x)
      ).lazy_covariance_matrix.evaluate_kernel()
      self.assertIsInstance(
          output, linear_operator.operators.AddedDiagLinearOperator
      )
  
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 15 for calling the TestAdditiveAndProductKernel obj's underlying member method code with example usage: obj.test_kernel_output() without return types