code: def __call__(self, img):
      ori_im = img.copy()
      data = {"image": img}
      data = transform(data, self.preprocess_op)
      img = data[0]
  
      if img is None:
          return None, 0
  
      img = np.expand_dims(img, axis=0)
      img = img.copy()
  
      preds, elapse = 0, 1
      starttime = time.time()
  
      self.input_tensor.copy_from_cpu(img)
      self.predictor.run()
  
      np_score_list, np_boxes_list = [], []
      output_names = self.predictor.get_output_names()
      num_outs = int(len(output_names) / 2)
      for out_idx in range(num_outs):
          np_score_list.append(
              self.predictor.get_output_handle(
                  output_names[out_idx]
              ).copy_to_cpu()
          )
          np_boxes_list.append(
              self.predictor.get_output_handle(
                  output_names[out_idx + num_outs]
              ).copy_to_cpu()
          )
      preds = dict(boxes=np_score_list, boxes_num=np_boxes_list)
  
      post_preds = self.postprocess_op(ori_im, img, preds)
      elapse = time.time() - starttime
      return post_preds, elapse
  
file_path: PaddleOCR/ppstructure/layout/predict_layout.py
imports: import os
  import sys
  import cv2
  import numpy
  import time
  import tools.infer.utility
  from ppocr.data import create_operators, transform
  from ppocr.postprocess import build_post_process
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppstructure.utility import parse_args
  from picodet_postprocess import PicoDetPostProcess
label_desc: Use image node_id 2 for calling the LayoutPredictor obj's underlying member method code with example usage: obj.__call__(img) and returns: post_preds,  elapse, None,  int