code: def __call__(self, img):
      starttime = time.time()
      ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
      if self.predictor is None:
          return ser_results, ser_elapse
  
      re_input, entity_idx_dict_batch = make_input(
          ser_inputs, ser_results
      )
      if self.use_visual_backbone == False:
          re_input.pop(4)
      for idx in range(len(self.input_tensor)):
          self.input_tensor[idx].copy_from_cpu(re_input[idx])
  
      self.predictor.run()
      outputs = []
      for output_tensor in self.output_tensors:
          output = output_tensor.copy_to_cpu()
          outputs.append(output)
      preds = dict(
          loss=outputs[1],
          pred_relations=outputs[2],
          hidden_states=outputs[0],
      )
  
      post_result = self.postprocess_op(
          preds,
          ser_results=ser_results,
          entity_idx_dict_batch=entity_idx_dict_batch,
      )
  
      elapse = time.time() - starttime
      return post_result, elapse
  
file_path: PaddleOCR/ppstructure/kie/predict_kie_token_ser_re.py
imports: import os
  import sys
  import cv2
  import json
  import numpy
  import time
  import tools.infer.utility
  from tools.infer_kie_token_ser_re import make_input
  from ppocr.postprocess import build_post_process
  from ppocr.utils.logging import get_logger
  from ppocr.utils.visual import draw_ser_results, draw_re_results
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppstructure.utility import parse_args
  from ppstructure.kie.predict_kie_token_ser import SerPredictor
label_desc: Use image node_id 2 for calling the SerRePredictor obj's underlying member method code with example usage: obj.__call__(img) and returns: post_result,  elapse, ser_results,  ser_elapse