code: def main():
      image_h = 640
      image_w = 640
      onnx_path = "yolov5s.onnx"
      ir_path = "ir_output/yolov5s.xml"
  
      image = np.random.randn(image_h, image_w, 3)
      normalized_image = normalize(image)
  
      # Convert the resized images to network input shape
      # [h, w, c] -> [c, h, w] -> [1, c, h, w]
      input_image = np.expand_dims(np.transpose(image, (2, 0, 1)), 0)
      normalized_input_image = np.expand_dims(
          np.transpose(normalized_image, (2, 0, 1)), 0
      )
  
      onnx_res = onnx_inference(onnx_path, normalized_input_image)
      ir_res = ir_inference(ir_path, input_image)
      np.testing.assert_allclose(
          onnx_res, ir_res, rtol=1e-03, atol=1e-05
      )
      print(
          "Exported model has been tested with OpenvinoRuntime, and the result looks good!"
      )
  
imports: import numpy
  import onnxruntime
  from openvino.runtime import Core
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types