code: def load_state_dict(model: nn.Module, weights_path: str) -> None:
      # '.'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 = torch.load(weights_path)
  
      num_classes = model.classifier.out_features
      load_fc = num_classes == 1000
  
      for key in list(state_dict.keys()):
          if load_fc is False:
              if "classifier" in key:
                  del state_dict[key]
  
          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, strict=load_fc)
      print("successfully load pretrain-weights.")
  
imports: import re
  from typing import Any, List, Tuple
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: load_state_dict(model, weights_path) without return types