def predict(self, repeats=1):
        '''
        Args:
            repeats (int): repeats number for prediction
        Returns:
            result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
                            shape: [N, 128]
        '''
        # model prediction
        np_pred_dets, np_pred_embs = None, None
        for i in range(repeats):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            boxes_tensor = self.predictor.get_output_handle(output_names[0])
            np_pred_dets = boxes_tensor.copy_to_cpu()
            embs_tensor = self.predictor.get_output_handle(output_names[1])
            np_pred_embs = embs_tensor.copy_to_cpu()

        result = dict(pred_dets=np_pred_dets, pred_embs=np_pred_embs)
        return result

        Args:
            repeats (int): repeats number for prediction
        Returns:
            result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
                            shape: [N, 128]
        
deploy/pptracking/python/mot_jde_infer.py