code: def load_weights_sequential(target, source_state):
      new_dict = OrderedDict()
      for (k1, v1), (k2, v2) in zip(
          target.state_dict().items(), source_state.items()
      ):
          new_dict[k1] = v2
      target.load_state_dict(new_dict)
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
label_desc: Use image node_id 1 for calling a global function with example usage: load_weights_sequential(target, source_state) without return types