code: def _load_state_dict(model, model_url, progress):
      # '.'s are no longer allowed in module names, but previous _DenseLayer
      # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
      # They are also in the checkpoints in model_urls. This pattern is used
      # to find such keys.
      pattern = re.compile(
          r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
      )
  
      state_dict = load_state_dict_from_url(
          model_url, progress=progress
      )
      for key in list(state_dict.keys()):
          res = pattern.match(key)
          if res:
              new_key = res.group(1) + res.group(2)
              state_dict[new_key] = state_dict[key]
              del state_dict[key]
      model.load_state_dict(state_dict)
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from collections import OrderedDict
  from torch.hub import load_state_dict_from_url
  from torch import Tensor
  from torch.jit.annotations import List
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 1 for calling a global function with example usage: _load_state_dict(model, model_url, progress) without return types