code: def test_regression_error(
      self,
      cuda=False,
      mll_cls=gpytorch.mlls.VariationalELBO,
      distribution_cls=gpytorch.variational.CholeskyVariationalDistribution,
  ):
      train_x, train_y = train_data(cuda=cuda)
      likelihood = GaussianLikelihood()
      model = SVGPRegressionModel(
          torch.linspace(0, 1, 25), distribution_cls
      )
      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=0.01,
      )
  
      _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(150):
              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)
  
          # Make sure CG was called (or not), and no warnings were thrown
          self.assertFalse(cg_mock.called)
  
          if (
              distribution_cls
              is gpytorch.variational.CholeskyVariationalDistribution
          ):
              # finally test fantasization
              # we only will check that tossing the entire training set into the model will reduce the mae
              model.likelihood = likelihood
              fant_model = model.get_fantasy_model(train_x, train_y)
              fant_preds = fant_model.likelihood(
                  fant_model(train_x)
              ).mean.squeeze()
              updated_abs_error = torch.mean(
                  torch.abs(train_y - fant_preds) / 2
              )
              # TODO: figure out why this error is worse than before
              self.assertLess(updated_abs_error.item(), 0.15)
  
imports: import math
  import unittest
  import warnings
  from pathlib import Path
  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
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.utils.warnings import OldVersionWarning
label_desc: Use image node_id 2 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_regression_error(cuda, mll_cls, distribution_cls) without return types