code: def detect(img_file, compiled_model, class_label):
      output = compiled_model.infer_new_request({0: test_image})
      result_ie = list(output.values())
  
      decode_boxes = []
      select_scores = []
      num_outs = int(len(result_ie) / 2)
      for out_idx in range(num_outs):
          decode_boxes.append(result_ie[out_idx])
          select_scores.append(result_ie[out_idx + num_outs])
  
      image = cv2.imread(img_file, 1)
      scale_x = image.shape[1] / test_image.shape[3]
      scale_y = image.shape[0] / test_image.shape[2]
  
      nms = PicoDetNMS(test_image.shape[2:], scale_x, scale_y)
      np_boxes = nms(decode_boxes, select_scores)
  
      res_image = draw_box(image, np_boxes, class_label)
  
      cv2.imwrite("res.jpg", res_image)
      cv2.imshow("res", res_image)
      cv2.waitKey()
  
file_path: PaddleDetection/deploy/third_engine/demo_openvino/python/openvino_infer.py
imports: import cv2
  import numpy
  import argparse
  from scipy.special import softmax
  from openvino.runtime import Core
label_desc: Use image node_id 7 for calling a global function with example usage: detect(img_file, compiled_model, class_label) without return types