code: def export_load_infer(args, model=None):
      """
      Export the ONNX model from PaddlePaddle, infer it by TRT.
      It checks the accuracy and tests the infer time.
  
      Args:
          args (dict): The input args.
          model (nn.Layer, optional): The paddle model to be exported and tested.
              If model is None, it creates a model with config file in args.
      """
  
      # 1. prepare
      if model is None:
          cfg = Config(args.config)
          builder = SegBuilder(cfg)
          model = builder.model
      if args.model_path is not None:
          utils.load_entire_model(model, args.model_path)
          logger.info("Loaded trained params of model successfully")
  
      model.eval()
      if args.print_model:
          print(model)
  
      input_shape = [1, 3, args.height, args.width]
      print("input shape:", input_shape)
      input_data = np.random.random(input_shape).astype("float32")
      model_name = os.path.basename(args.config).split(".")[0]
  
      # 2. run paddle
      paddle_out = run_paddle(model, input_data)
      print("out shape:", paddle_out.shape)
      print("The paddle model has been predicted by PaddlePaddle.\n")
  
      # 3. export onnx
      input_spec = paddle.static.InputSpec(input_shape, "float32", "x")
      onnx_model_path = os.path.join(
          args.save_dir, model_name + "_model"
      )
      paddle.onnx.export(
          model,
          onnx_model_path,
          input_spec=[input_spec],
          opset_version=11,
      )
      print("Completed export onnx model.\n")
  
      # 4. run and check onnx
      onnx_model_path = onnx_model_path + ".onnx"
      onnx_out = check_and_run_onnx(onnx_model_path, input_data)
      assert onnx_out.shape == paddle_out.shape
      np.testing.assert_allclose(
          onnx_out, paddle_out, rtol=0, atol=1e-03
      )
      print("The paddle and onnx models have the same outputs.\n")
  
      # 5. run and check trt
      assert args.trt_version in (5, 7), "trt_version should be 5 or 7"
      if args.trt_version == 5:
          trt_out, latency = TRTPredictorV2().trt5_run(
              args, onnx_model_path, input_data
          )
      elif args.trt_version == 7:
          trt_out, latency = TRTPredictorV2().trt7_run(
              args, onnx_model_path, input_data
          )
      print("trt avg latency: {:.3f} ms".format(latency))
  
      assert trt_out.size == paddle_out.size
      trt_out = trt_out.reshape(paddle_out.shape)
      np.testing.assert_allclose(
          trt_out, paddle_out, rtol=0, atol=1e-03
      )
      print("The paddle and trt models have the same outputs.\n")
  
      return latency
  
imports: import argparse
  import os
  import time
  import numpy
  import paddle
  import tensorrt
  import pycuda.driver
  import pycuda.autoinit
  import onnx
  import onnxruntime
  from paddleseg.cvlibs import Config, SegBuilder
  from paddleseg.utils import logger, utils
label_desc: Use image node_id 4 for calling a global function with example usage: export_load_infer(args, model) and returns: latency