code: def _load_flexible_state_dict(model: Module, path: str) -> float:
      r"""
      Helper to load pytorch models. This function attempts to find compatibility for
      loading models that were trained on different devices / with DataParallel but are
      being loaded in a different environment.
  
      Assumes that the model has been saved as a state_dict in some capacity. This can
      either be a single state dict, or a nesting dictionary which contains the model
      state_dict and other information.
  
      Args:
  
          model (torch.nn.Module): The model for which to load a checkpoint
          path (str): The filepath to the checkpoint
  
      The module state_dict is modified in-place, and the learning rate is returned.
      """
  
      checkpoint = torch.load(path)
  
      learning_rate = checkpoint.get("learning_rate", 1.0)
      # can get learning rate from optimizer state_dict?
  
      if "module." in next(iter(checkpoint)):
          if isinstance(model, nn.DataParallel):
              model.load_state_dict(checkpoint)
          else:
              model = nn.DataParallel(model)
              model.load_state_dict(checkpoint)
              model = model.module
      else:
          if isinstance(model, nn.DataParallel):
              model = model.module
              model.load_state_dict(checkpoint)
              model = nn.DataParallel(model)
          else:
              model.load_state_dict(checkpoint)
  
      return learning_rate
  
imports: import warnings
  from functools import reduce
  from typing import Any, Callable, Iterable, List, NamedTuple, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn
  from captum._utils.common import _get_module_from_name, _parse_version
  from captum._utils.gradient import _compute_jacobian_wrt_params, _compute_jacobian_wrt_params_with_sample_wise_trick
  from captum._utils.progress import progress
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 4 for calling a global function with example usage: _load_flexible_state_dict(model, path) and returns: learning_rate