def METHOD_NAME(self, inp_h, inp_w):
    # For better efficiency, model is initialized for batch_size 1 and every sample processed independently
    inp_shape = [1, inp_h, inp_w, 3]
    self.net.reshape({self.input_name: inp_shape})
    # Load network to device
    if "CPU" in self.device:
        self.core.set_property(
            "CPU",
            {
                "CPU_THROUGHPUT_STREAMS": "CPU_THROUGHPUT_AUTO",
                "CPU_BIND_THREAD": "YES",
            },
        )
    if "GPU" in self.device:
        self.core.set_property(
            "GPU", {"GPU_THROUGHPUT_STREAMS": "GPU_THROUGHPUT_AUTO"}
        )
    compiled_model = self.core.compile_model(self.net, self.device)
    num_requests = compiled_model.get_property("OPTIMAL_NUMBER_OF_INFER_REQUESTS")
    print(f"OpenVINO uses {num_requests} inference requests")
    self.infer_queue = AsyncInferQueue(compiled_model, num_requests)