code: def test_classification_error(
      self, cuda=False, mll_cls=gpytorch.mlls.VariationalELBO
  ):
      train_x, train_y = train_data(cuda=cuda)
      likelihood = BernoulliLikelihood()
      model = SVGPClassificationModel(torch.linspace(0, 1, 25))
      mll = mll_cls(likelihood, model, num_data=len(train_y))
      if cuda:
          likelihood = likelihood.cuda()
          model = model.cuda()
          mll = mll.cuda()
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
      optimizer = optim.Adam(
          [
              {"params": model.parameters()},
              {"params": likelihood.parameters()},
          ],
          lr=0.1,
      )
  
      _wrapped_cg = MagicMock(wraps=linear_operator.utils.linear_cg)
      _cg_mock = patch(
          "linear_operator.utils.linear_cg", new=_wrapped_cg
      )
      with _cg_mock as cg_mock:
          for _ in range(400):
              optimizer.zero_grad()
              output = model(train_x)
              loss = -mll(output, train_y)
              loss.backward()
              optimizer.step()
  
          for param in model.parameters():
              self.assertTrue(param.grad is not None)
              self.assertGreater(param.grad.norm().item(), 0)
          for param in likelihood.parameters():
              self.assertTrue(param.grad is not None)
              self.assertGreater(param.grad.norm().item(), 0)
  
          # Set back to eval mode
          model.eval()
          likelihood.eval()
          test_preds = (
              likelihood(model(train_x)).mean.squeeze().round().float()
          )
          mean_abs_error = torch.mean(
              torch.ne(train_y, test_preds).float()
          )
          self.assertLess(mean_abs_error.item(), 2e-1)
  
          self.assertFalse(cg_mock.called)
  
imports: import math
  import unittest
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.likelihoods import BernoulliLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
label_desc: Use image node_id 1 for calling the TestSVGPClassification obj's underlying member method code with example usage: obj.test_classification_error(cuda, mll_cls) without return types