code: def get_center(model, eval_dataloader, post_process_class):
      pbar = tqdm(total=len(eval_dataloader), desc="get center:")
      max_iter = (
          len(eval_dataloader) - 1
          if platform.system() == "Windows"
          else len(eval_dataloader)
      )
      char_center = dict()
      for idx, batch in enumerate(eval_dataloader):
          if idx >= max_iter:
              break
          images = batch[0]
          start = time.time()
          preds = model(images)
  
          batch = [item.numpy() for item in batch]
          # Obtain usable results from post-processing methods
          post_result = post_process_class(preds, batch[1])
  
          # update char_center
          char_center = update_center(char_center, post_result, preds)
          pbar.update(1)
  
      pbar.close()
      for key in char_center.keys():
          char_center[key] = char_center[key][0]
      return char_center
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import platform
  import yaml
  import time
  import datetime
  import paddle
  import paddle.distributed
  from tqdm import tqdm
  import cv2
  import numpy
  from argparse import ArgumentParser, RawDescriptionHelpFormatter
  from ppocr.utils.stats import TrainingStats
  from ppocr.utils.save_load import save_model
  from ppocr.utils.utility import print_dict, AverageMeter
  from ppocr.utils.logging import get_logger
  from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers
  from ppocr.utils import profiler
  from ppocr.data import build_dataloader
label_desc: Use image node_id 8 for calling a global function with example usage: get_center(model, eval_dataloader, post_process_class) and returns: char_center