code: def test_loading_old_model(self):
      train_x, train_y = train_data(cuda=False)
      likelihood = GaussianLikelihood()
      model = SVGPRegressionModel(
          torch.linspace(0, 1, 25),
          gpytorch.variational.CholeskyVariationalDistribution,
      )
      data_file = (
          Path(__file__)
          .parent.joinpath("old_variational_strategy_model.pth")
          .resolve()
      )
      state_dicts = torch.load(data_file)
      likelihood.load_state_dict(
          state_dicts["likelihood"], strict=False
      )
  
      # Ensure we get a warning
      with warnings.catch_warnings(record=True) as ws:
          warnings.simplefilter("always", OldVersionWarning)
  
          model.load_state_dict(state_dicts["model"])
          self.assertTrue(
              any(issubclass(w.category, OldVersionWarning) for w in ws)
          )
  
      with torch.no_grad():
          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)
  
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 1 for calling the TestSVGPRegression obj's underlying member method code with example usage: obj.test_loading_old_model() without return types