code: def test_regression_error(
      self,
      mll_cls=gpytorch.mlls.VariationalELBO,
      distribution_cls=gpytorch.variational.CholeskyVariationalDistribution,
  ):
      train_x, train_y = train_data()
      likelihood = GaussianLikelihood()
      model = SVGPRegressionModel(
          torch.linspace(0, 1, 128), torch.linspace(0, 1, 16)
      )
      mll = mll_cls(likelihood, model, num_data=len(train_y))
  
      # Find optimal model hyperparameters
      model.train()
      likelihood.train()
      optimizer = optim.Adam(
          [
              {"params": model.parameters()},
              {"params": likelihood.parameters()},
          ],
          lr=0.01,
      )
  
      _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(75):
              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()
          mean_abs_error = torch.mean(
              torch.abs(train_y - test_preds) / 2
          )
          self.assertLess(mean_abs_error.item(), 1e-1)
  
          self.assertFalse(cg_mock.called)
  
file_path: gpytorch/test/examples/test_decoupled_svgp_regression.py
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 GaussianLikelihood
  from gpytorch.models import ApproximateGP
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_regression_error(mll_cls, distribution_cls) without return types