code: def _training_iter(
      self,
      model,
      likelihood,
      batch_shape=torch.Size([]),
      mll_cls=gpytorch.mlls.VariationalELBO,
      cuda=False,
  ):
      train_x = torch.randn(*batch_shape, 32, 2).clamp(-2.5, 2.5)
      train_y = torch.linspace(-1, 1, self.event_shape[0])
      train_y = train_y.view(
          self.event_shape[0], *([1] * (len(self.event_shape) - 1))
      )
      train_y = train_y.expand(*self.event_shape)
      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: from abc import abstractproperty
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  import gpytorch
  from .base_test_case import BaseTestCase
label_desc: Use image node_id 2 for calling the VariationalTestCase obj's underlying member method code with example usage: obj._training_iter(model, likelihood, batch_shape, mll_cls, cuda) and returns: output,  loss