code: def save_conv_bn(fp, conv_model, bn_model):
      if bn_model.bias.is_cuda:
          convert2cpu(bn_model.bias.data).numpy().tofile(fp)
          convert2cpu(bn_model.weight.data).numpy().tofile(fp)
          convert2cpu(bn_model.running_mean).numpy().tofile(fp)
          convert2cpu(bn_model.running_var).numpy().tofile(fp)
          convert2cpu(conv_model.weight.data).numpy().tofile(fp)
      else:
          bn_model.bias.data.numpy().tofile(fp)
          bn_model.weight.data.numpy().tofile(fp)
          bn_model.running_mean.numpy().tofile(fp)
          bn_model.running_var.numpy().tofile(fp)
          conv_model.weight.data.numpy().tofile(fp)
  
file_path: MMdnn/mmdnn/conversion/darknet/cfg.py
imports: import torch
  from collections import OrderedDict
label_desc: Use image node_id 8 for calling a global function with example usage: save_conv_bn(fp, conv_model, bn_model) without return types