code: def convert(src, dst, depth):
      """Convert keys in detectron pretrained ResNet models to pytorch style."""
      # load arch_settings
      if depth not in arch_settings:
          raise ValueError(
              "Only support ResNet-50 and ResNet-101 currently"
          )
      block_nums = arch_settings[depth]
      # load caffe model
      caffe_model = load(src, encoding="latin1")
      blobs = (
          caffe_model["blobs"]
          if "blobs" in caffe_model
          else caffe_model
      )
      # convert to pytorch style
      state_dict = OrderedDict()
      converted_names = set()
      convert_conv_fc(
          blobs, state_dict, "conv1", "conv1", converted_names
      )
      convert_bn(
          blobs, state_dict, "res_conv1_bn", "bn1", converted_names
      )
      for i in range(1, len(block_nums) + 1):
          for j in range(block_nums[i - 1]):
              if j == 0:
                  convert_conv_fc(
                      blobs,
                      state_dict,
                      f"res{i + 1}_{j}_branch1",
                      f"layer{i}.{j}.downsample.0",
                      converted_names,
                  )
                  convert_bn(
                      blobs,
                      state_dict,
                      f"res{i + 1}_{j}_branch1_bn",
                      f"layer{i}.{j}.downsample.1",
                      converted_names,
                  )
              for k, letter in enumerate(["a", "b", "c"]):
                  convert_conv_fc(
                      blobs,
                      state_dict,
                      f"res{i + 1}_{j}_branch2{letter}",
                      f"layer{i}.{j}.conv{k+1}",
                      converted_names,
                  )
                  convert_bn(
                      blobs,
                      state_dict,
                      f"res{i + 1}_{j}_branch2{letter}_bn",
                      f"layer{i}.{j}.bn{k + 1}",
                      converted_names,
                  )
      # check if all layers are converted
      for key in blobs:
          if key not in converted_names:
              print(f"Not Convert: {key}")
      # save checkpoint
      checkpoint = dict()
      checkpoint["state_dict"] = state_dict
      torch.save(checkpoint, dst)
  
imports: import argparse
  from collections import OrderedDict
  import torch
  from mmengine.fileio import load
label_desc: Use image node_id 3 for calling a global function with example usage: convert(src, dst, depth) without return types