code: def __call__(self, data):
      with open(data["img_path"], "rb") as f:
          img = f.read()
      data["image"] = img
      batch = transform(data, self.ops)
      batch = to_tensor(batch)
      preds = self.model(batch)
  
      post_result = self.post_process_class(
          preds, segment_offset_ids=batch[6], ocr_infos=batch[7]
      )
      return post_result, batch
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import os
  import sys
  import cv2
  import json
  import paddle
  from ppocr.data import create_operators, transform
  from ppocr.modeling.architectures import build_model
  from ppocr.postprocess import build_post_process
  from ppocr.utils.save_load import load_model
  from ppocr.utils.visual import draw_ser_results
  from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps
  import tools.program
label_desc: Use image node_id 2 for calling the SerPredictor obj's underlying member method code with example usage: obj.__call__(data) and returns: post_result,  batch