code: def load_conv_bn(buf, start, conv_model, bn_model):
      num_w = conv_model.weight.numel()
      num_b = bn_model.bias.numel()
      bn_model.bias.data.copy_(
          torch.from_numpy(buf[start : start + num_b])
      )
      start = start + num_b
      bn_model.weight.data.copy_(
          torch.from_numpy(buf[start : start + num_b])
      )
      start = start + num_b
      bn_model.running_mean.copy_(
          torch.from_numpy(buf[start : start + num_b])
      )
      start = start + num_b
      bn_model.running_var.copy_(
          torch.from_numpy(buf[start : start + num_b])
      )
      start = start + num_b
      conv_model.weight.data.copy_(
          torch.from_numpy(buf[start : start + num_w])
      )
      start = start + num_w
      return start
  
file_path: MMdnn/mmdnn/conversion/darknet/cfg.py
imports: import torch
  from collections import OrderedDict
label_desc: Use image node_id 7 for calling a global function with example usage: load_conv_bn(buf, start, conv_model, bn_model) and returns: start