code: def test_backward_train(self):
      data = self.create_test_data()
      likelihood, labels = self.create_likelihood_and_labels()
      model = self.create_model(data, labels, likelihood)
      mll = gpytorch.mlls.VariationalELBO(
          likelihood, model, num_data=labels.size(-1)
      )
      model.train()
      likelihood.train()
  
      # We'll just do one step of gradient descent to mix up the params a bit
      optimizer = torch.optim.Adam(
          [
              {"params": model.parameters()},
              {"params": likelihood.parameters()},
          ],
          lr=0.01,
      )
  
      output = model(data)
      loss = -mll(output, labels)
      loss.backward()
      optimizer.step()
      optimizer.zero_grad()
      output = model(data)
      loss = -mll(output, labels)
      loss.backward()
  
      for _, param in model.named_parameters():
          self.assertTrue(param.grad is not None)
          self.assertGreater(param.grad.norm().item(), 0)
      for _, param in likelihood.named_parameters():
          self.assertTrue(param.grad is not None)
          self.assertGreater(param.grad.norm().item(), 0)
      optimizer.step()
  
imports: from abc import abstractmethod
  import torch
  import gpytorch
label_desc: Use image node_id 1 for calling the VariationalModelTestCase obj's underlying member method code with example usage: obj.test_backward_train() without return types