code: def convert(src, dst):
      """Convert keys in pycls pretrained RegNet models to mmdet style."""
      # load caffe model
      regnet_model = torch.load(src)
      blobs = regnet_model["model_state"]
      # convert to pytorch style
      state_dict = OrderedDict()
      converted_names = set()
      for key, weight in blobs.items():
          if "stem" in key:
              convert_stem(key, weight, state_dict, converted_names)
          elif "head" in key:
              convert_head(key, weight, state_dict, converted_names)
          elif key.startswith("s"):
              convert_reslayer(key, weight, state_dict, converted_names)
  
      # check if all layers are converted
      for key in blobs:
          if key not in converted_names:
              print(f"not converted: {key}")
      # save checkpoint
      checkpoint = dict()
      checkpoint["state_dict"] = state_dict
      torch.save(checkpoint, dst)
  
imports: import argparse
  from collections import OrderedDict
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: convert(src, dst) without return types