code: def convert_conv_bn(new_weight, prefix, ks, v):
      if "conv" in ks[0]:
          if "weight" == ks[1]:
              new_weight[prefix + ".conv.weight"] = v
          elif "bias" == ks[1]:
              new_weight[prefix + ".conv.bias"] = v
          else:
              print(f"unrecognized weight {prefix + ks[1]}")
          return
  
      if "bn" in ks[0]:
          if "running_mean" == ks[1]:
              new_weight[prefix + ".bn.running_mean"] = v
          elif "running_var" == ks[1]:
              new_weight[prefix + ".bn.running_var"] = v
          elif "weight" == ks[1]:
              new_weight[prefix + ".bn.weight"] = v
          elif "bias" == ks[1]:
              new_weight[prefix + ".bn.bias"] = v
          elif "num_batches_tracked" == ks[1]:
              return
          else:
              print(f"unrecognized weight {prefix + ks[1]}")
          return
  
imports: import re
  import torch
  from src import u2net_full, u2net_lite
label_desc: Use image node_id 1 for calling a global function with example usage: convert_conv_bn(new_weight, prefix, ks, v) without return types