code: def ir_inference(ir_path: str, image: np.ndarray):
      # Load the network in Inference Engine
      ie = Core()
      model_ir = ie.read_model(model=ir_path)
      compiled_model_ir = ie.compile_model(
          model=model_ir, device_name="CPU"
      )
  
      # Get input and output layers
      input_layer_ir = next(iter(compiled_model_ir.inputs))
      output_layer_ir = next(iter(compiled_model_ir.outputs))
  
      # Run inference on the input image
      res_ir = compiled_model_ir([image])[output_layer_ir]
      return res_ir
  
imports: import numpy
  import onnxruntime
  from openvino.runtime import Core
label_desc: Use image node_id 3 for calling a global function with example usage: ir_inference(ir_path, image) and returns: res_ir