code: def convert_bn(
      blobs, state_dict, caffe_name, torch_name, converted_names
  ):
      # detectron replace bn with affine channel layer
      state_dict[torch_name + ".bias"] = torch.from_numpy(
          blobs[caffe_name + "_b"]
      )
      state_dict[torch_name + ".weight"] = torch.from_numpy(
          blobs[caffe_name + "_s"]
      )
      bn_size = state_dict[torch_name + ".weight"].size()
      state_dict[torch_name + ".running_mean"] = torch.zeros(bn_size)
      state_dict[torch_name + ".running_var"] = torch.ones(bn_size)
      converted_names.add(caffe_name + "_b")
      converted_names.add(caffe_name + "_s")
  
imports: import argparse
  from collections import OrderedDict
  import torch
  from mmengine.fileio import load
label_desc: Use image node_id 1 for calling a global function with example usage: convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names) without return types