code: def test_added_loss_term(self):
      # This loss term won't usually be called with diagonal MVNs
      # However, the loss term only accesses the diagonals of the MVN covariance matrices
      # So we're simplifying the setup for the unit test
      prior_dist = MultivariateNormal(
          torch.zeros(5),
          DiagLinearOperator(torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0])),
      )
      variational_dist = MultivariateNormal(
          torch.zeros(5),
          DiagLinearOperator(torch.tensor([0.6, 0.7, 0.8, 0.9, 1.0])),
      )
      likelihood = GaussianLikelihood()
      likelihood.noise = 0.01
  
      added_loss_term = InducingPointKernelAddedLossTerm(
          prior_dist, variational_dist, likelihood
      )
      self.assertAllClose(added_loss_term.loss(), torch.tensor(-50.0))
  
file_path: gpytorch/test/mlls/test_inducing_point_kernel_added_loss_term.py
imports: import unittest
  import torch
  from linear_operator.operators import DiagLinearOperator
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import InducingPointKernelAddedLossTerm
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 for calling the TestInducingPointKernelAddedLossTerm obj's underlying member method code with example usage: obj.test_added_loss_term() without return types