code: def check_and_run_onnx(onnx_model_path, input_data):
      onnx_model = onnx.load(onnx_model_path)
      onnx.checker.check_model(onnx_model)
      print("The onnx model has been checked.")
  
      ort_sess = onnxruntime.InferenceSession(onnx_model_path)
      ort_inputs = {ort_sess.get_inputs()[0].name: input_data}
      ort_outs = ort_sess.run(None, ort_inputs)
      print("The onnx model has been predicted by ONNXRuntime.")
  
      return ort_outs[0]
  
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 3 for calling a global function with example usage: check_and_run_onnx(onnx_model_path, input_data) and returns: ort_outs