code: def pg_postprocess_fast(self):
      p_score = self.outs_dict["f_score"]
      p_border = self.outs_dict["f_border"]
      p_char = self.outs_dict["f_char"]
      p_direction = self.outs_dict["f_direction"]
      if isinstance(p_score, paddle.Tensor):
          p_score = p_score[0].numpy()
          p_border = p_border[0].numpy()
          p_direction = p_direction[0].numpy()
          p_char = p_char[0].numpy()
      else:
          p_score = p_score[0]
          p_border = p_border[0]
          p_direction = p_direction[0]
          p_char = p_char[0]
  
      src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
      instance_yxs_list, seq_strs = generate_pivot_list_fast(
          p_score,
          p_char,
          p_direction,
          self.Lexicon_Table,
          score_thresh=self.score_thresh,
          point_gather_mode=self.point_gather_mode,
      )
      poly_list, keep_str_list = restore_poly(
          instance_yxs_list,
          seq_strs,
          p_border,
          ratio_w,
          ratio_h,
          src_w,
          src_h,
          self.valid_set,
      )
      data = {
          "points": poly_list,
          "texts": keep_str_list,
      }
      return data
  
file_path: PaddleOCR/ppocr/utils/e2e_utils/pgnet_pp_utils.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import os
  import sys
  from extract_textpoint_slow import *
  from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
label_desc: Use image node_id 2 for calling the PGNet_PostProcess obj's underlying member method code with example usage: obj.pg_postprocess_fast() and returns: data