class_docstr: Standardize outcomes (zero mean, unit variance).
  
  This module is stateful: If in train mode, calling forward updates the
  module state (i.e. the mean/std normalizing constants). If in eval mode,
  calling forward simply applies the standardization using the current module
  state.
docstr: Custom logic for loading the state dict.
docstr_tok: ["Custom","logic","for","loading","the","state","dict","."]
code: def load_state_dict(
      self, state_dict: Mapping[str, Any], strict: bool = True
  ) -> None:
      r"""Custom logic for loading the state dict."""
      if "_is_trained" not in state_dict:
          warnings.warn(
              "Key '_is_trained' not found in state_dict. Setting to True. "
              "In a future release, this will result in an error.",
              DeprecationWarning,
          )
          state_dict = {**state_dict, "_is_trained": torch.tensor(True)}
      super().load_state_dict(state_dict, strict=strict)
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional, Tuple, Union
  import torch
  from botorch.models.transforms.utils import norm_to_lognorm_mean, norm_to_lognorm_variance
  from botorch.posteriors import GPyTorchPosterior, Posterior, TransformedPosterior
  from botorch.utils.transforms import normalize_indices
  from linear_operator.operators import CholLinearOperator, DiagLinearOperator
  from torch import Tensor
  from torch.nn import Module, ModuleDict
label_desc: Use image node_id 2 for calling the Standardize obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) without return types