code: def main():
      args = parser.parse_args()
      args.gpu_id = 0
      if args.c2_prefix:
          args.c2_init = args.c2_prefix + ".init.pb"
          args.c2_predict = args.c2_prefix + ".predict.pb"
  
      model = model_helper.ModelHelper(name="le_net", init_params=False)
  
      # Bring in the init net from init_net.pb
      init_net_proto = caffe2_pb2.NetDef()
      with open(args.c2_init, "rb") as f:
          init_net_proto.ParseFromString(f.read())
      model.param_init_net = core.Net(init_net_proto)
  
      # bring in the predict net from predict_net.pb
      predict_net_proto = caffe2_pb2.NetDef()
      with open(args.c2_predict, "rb") as f:
          predict_net_proto.ParseFromString(f.read())
      model.net = core.Net(predict_net_proto)
  
      # CUDA performance not impressive
      # device_opts = core.DeviceOption(caffe2_pb2.PROTO_CUDA, args.gpu_id)
      # model.net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
      # model.param_init_net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
  
      input_blob = model.net.external_inputs[0]
      model.param_init_net.GaussianFill(
          [],
          input_blob.GetUnscopedName(),
          shape=(args.batch_size, 3, args.img_size, args.img_size),
          mean=0.0,
          std=1.0,
      )
      workspace.RunNetOnce(model.param_init_net)
      workspace.CreateNet(model.net, overwrite=True)
      workspace.BenchmarkNet(model.net.Proto().name, 5, 20, True)
  
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types