code: def get_state_dict(
      module, destination=None, prefix="", keep_vars=False
  ):
      """Returns a dictionary containing a whole state of the module.
  
      Both parameters and persistent buffers (e.g. running averages) are
      included. Keys are corresponding parameter and buffer names.
  
      This method is modified from :meth:`torch.nn.Module.state_dict` to
      recursively check parallel module in case that the model has a complicated
      structure, e.g., nn.Module(nn.Module(DDP)).
  
      Args:
          module (nn.Module): The module to generate state_dict.
          destination (OrderedDict): Returned dict for the state of the
              module.
          prefix (str): Prefix of the key.
          keep_vars (bool): Whether to keep the variable property of the
              parameters. Default: False.
  
      Returns:
          dict: A dictionary containing a whole state of the module.
      """
      # recursively check parallel module in case that the model has a
      # complicated structure, e.g., nn.Module(nn.Module(DDP))
      if is_module_wrapper(module):
          module = module.module
  
      # below is the same as torch.nn.Module.state_dict()
      if destination is None:
          destination = OrderedDict()
          destination._metadata = OrderedDict()
      destination._metadata[prefix[:-1]] = local_metadata = dict(
          version=module._version
      )
      _save_to_state_dict(module, destination, prefix, keep_vars)
      for name, child in module._modules.items():
          if child is not None:
              get_state_dict(
                  child,
                  destination,
                  prefix + name + ".",
                  keep_vars=keep_vars,
              )
      for hook in module._state_dict_hooks.values():
          hook_result = hook(
              module, destination, prefix, local_metadata
          )
          if hook_result is not None:
              destination = hook_result
      return destination
  
imports: import io
  import os
  import os.path
  import pkgutil
  import time
  import warnings
  from collections import OrderedDict
  from importlib import import_module
  from tempfile import TemporaryDirectory
  import torch
  import torchvision
  from torch.optim import Optimizer
  from torch.utils import model_zoo
  from torch.nn import functional
  import annotator.uniformer.mmcv
  from annotator.uniformer.mmcv.fileio import FileClient
  from annotator.uniformer.mmcv.fileio import load
  from annotator.uniformer.mmcv.parallel import is_module_wrapper
  from annotator.uniformer.mmcv.utils import mkdir_or_exist
  from annotator.uniformer.mmcv.runner import get_dist_info
label_desc: Use image node_id 15 for calling a global function with example usage: get_state_dict(module, destination, prefix, keep_vars) and returns: destination