code: def onnx_inference(args):
      import onnxruntime
  
      ort_sess = onnxruntime.InferenceSession(args.onnx_file)
  
      img = cv2.imread(args.image_path)
      # normalize to mean 0.5, std 0.5
      img = (img - 127.5) * 0.00784313725
      # BGR2RGB
      img = img[:, :, ::-1]
      img = img.transpose((2, 0, 1))
      img = np.expand_dims(img, 0)
      img = img.astype("float32")
  
      ort_inputs = {ort_sess.get_inputs()[0].name: img}
      ort_outs = ort_sess.run(None, ort_inputs)
  
      print("onnx inference result: ", ort_outs[0].shape)
  
file_path: insightface/recognition/arcface_paddle/tools/inference.py
imports: import sys
  import os
  import cv2
  import time
  import json
  import argparse
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: onnx_inference(args) without return types