code: def test_loading_old_module(self):
      batch_shape = self.__class__.batch_shape or torch.Size([])
      constant = torch.randn(batch_shape)
      mean = self.create_mean()
      model = _GPModel(mean)
  
      old_state_dict = OrderedDict(
          [
              ("likelihood.noise_covar.raw_noise", torch.tensor([0.0])),
              (
                  "likelihood.noise_covar.raw_noise_constraint.lower_bound",
                  torch.tensor(1.0000e-04),
              ),
              (
                  "likelihood.noise_covar.raw_noise_constraint.upper_bound",
                  torch.tensor(math.inf),
              ),
              ("mean_module.constant", constant.unsqueeze(-1)),
          ]
      )
      with warnings.catch_warnings(record=True) as ws:
          warnings.simplefilter("always", OldVersionWarning)
          model.load_state_dict(old_state_dict)
          self.assertTrue(
              any(issubclass(w.category, OldVersionWarning) for w in ws)
          )
          self.assertEqual(model.mean_module.constant.data, constant)
  
      new_state_dict = OrderedDict(
          [
              ("likelihood.noise_covar.raw_noise", torch.tensor([0.0])),
              (
                  "likelihood.noise_covar.raw_noise_constraint.lower_bound",
                  torch.tensor(1.0000e-04),
              ),
              (
                  "likelihood.noise_covar.raw_noise_constraint.upper_bound",
                  torch.tensor(math.inf),
              ),
              ("mean_module.raw_constant", constant),
          ]
      )
      with warnings.catch_warnings(record=True) as ws:
          warnings.simplefilter("always", OldVersionWarning)
          model.load_state_dict(new_state_dict)
          self.assertFalse(
              any(issubclass(w.category, OldVersionWarning) for w in ws)
          )
  
imports: import math
  import pickle
  import unittest
  import warnings
  from collections import OrderedDict
  import torch
  import gpytorch
  from gpytorch.constraints import GreaterThan
  from gpytorch.means import ConstantMean
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_mean_test_case import BaseMeanTestCase
  from gpytorch.utils.warnings import OldVersionWarning
label_desc: Use image node_id 4 for calling the TestConstantMean obj's underlying member method code with example usage: obj.test_loading_old_module() without return types