code: def test_standardize_state_dict(self):
      for m in (1, 2):
          with self.subTest(m=2):
              transform = Standardize(m=m)
              self.assertFalse(transform._is_trained)
              self.assertTrue(transform.training)
              Y = torch.rand(2, m)
              transform(Y)
              state_dict = transform.state_dict()
              new_transform = Standardize(m=m)
              self.assertFalse(new_transform._is_trained)
              new_transform.load_state_dict(state_dict)
              self.assertTrue(new_transform._is_trained)
              # test deprecation error when loading state dict without _is_trained
              state_dict.pop("_is_trained")
              with self.assertWarnsRegex(
                  DeprecationWarning,
                  "Key '_is_trained' not found in state_dict. Setting to True.",
              ):
                  new_transform.load_state_dict(state_dict)
  
imports: import itertools
  from copy import deepcopy
  import torch
  from botorch.models.transforms.outcome import Bilog, ChainedOutcomeTransform, Log, OutcomeTransform, Power, Standardize
  from botorch.models.transforms.utils import norm_to_lognorm_mean, norm_to_lognorm_variance
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, DiagLinearOperator
label_desc: Use image node_id 5 for calling the TestOutcomeTransforms obj's underlying member method code with example usage: obj.test_standardize_state_dict() without return types