code: def __call__(self, img):
      ori_im = img.copy()
      data = {"image": img}
      data = transform(data, self.preprocess_op)
      img, shape_list = data
      if img is None:
          return None, 0
      img = np.expand_dims(img, axis=0)
      shape_list = np.expand_dims(shape_list, axis=0)
      img = img.copy()
      starttime = time.time()
  
      if self.use_onnx:
          input_dict = {}
          input_dict[self.input_tensor.name] = img
          outputs = self.predictor.run(self.output_tensors, input_dict)
          preds = {}
          preds["f_border"] = outputs[0]
          preds["f_char"] = outputs[1]
          preds["f_direction"] = outputs[2]
          preds["f_score"] = outputs[3]
      else:
          self.input_tensor.copy_from_cpu(img)
          self.predictor.run()
          outputs = []
          for output_tensor in self.output_tensors:
              output = output_tensor.copy_to_cpu()
              outputs.append(output)
  
          preds = {}
          if self.e2e_algorithm == "PGNet":
              preds["f_border"] = outputs[0]
              preds["f_char"] = outputs[1]
              preds["f_direction"] = outputs[2]
              preds["f_score"] = outputs[3]
          else:
              raise NotImplementedError
      post_result = self.postprocess_op(preds, shape_list)
      points, strs = post_result["points"], post_result["texts"]
      dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
      elapse = time.time() - starttime
      return dt_boxes, strs, elapse
  
file_path: PaddleOCR/tools/infer/predict_e2e.py
imports: import os
  import sys
  import cv2
  import numpy
  import time
  import sys
  import tools.infer.utility
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read
  from ppocr.data import create_operators, transform
  from ppocr.postprocess import build_post_process
label_desc: Use image node_id 4 for calling the TextE2E obj's underlying member method code with example usage: obj.__call__(img) and returns: dt_boxes,  strs,  elapse, None,  int