code: def _training_iter(
      self,
      model,
      likelihood,
      batch_shape=torch.Size([]),
      mll_cls=gpytorch.mlls.VariationalELBO,
      cuda=False,
  ):
      # We cannot inheret the superclass method
      # Because it sets the training data to be the inducing points
  
      train_x = model.variational_strategy.inducing_points
      train_y = torch.randn(train_x.shape[:-1])
      mll = mll_cls(likelihood, model, num_data=train_x.size(-2))
      if cuda:
          train_x = train_x.cuda()
          train_y = train_y.cuda()
          model = model.cuda()
          likelihood = likelihood.cuda()
  
      # Single optimization iteration
      model.train()
      likelihood.train()
      output = model(train_x)
      loss = -mll(output, train_y)
      loss.sum().backward()
  
      # Make sure we have gradients for all parameters
      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)
  
      return output, loss
  
imports: import unittest
  import torch
  import gpytorch
  from gpytorch.test.variational_test_case import VariationalTestCase
label_desc: Use image node_id 8 for calling the TestVNNGP obj's underlying member method code with example usage: obj._training_iter(model, likelihood, batch_shape, mll_cls, cuda) and returns: output,  loss