code: def __init__(self, args):
      self.rec_image_shape = [
          int(v) for v in args.rec_image_shape.split(",")
      ]
      self.rec_batch_num = args.rec_batch_num
      self.rec_algorithm = args.rec_algorithm
      postprocess_params = {
          "name": "CTCLabelDecode",
          "character_dict_path": args.rec_char_dict_path,
          "use_space_char": args.use_space_char,
      }
      if self.rec_algorithm == "SRN":
          postprocess_params = {
              "name": "SRNLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "RARE":
          postprocess_params = {
              "name": "AttnLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "NRTR":
          postprocess_params = {
              "name": "NRTRLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "SAR":
          postprocess_params = {
              "name": "SARLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "VisionLAN":
          postprocess_params = {
              "name": "VLLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "ViTSTR":
          postprocess_params = {
              "name": "ViTSTRLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "ABINet":
          postprocess_params = {
              "name": "ABINetLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "SPIN":
          postprocess_params = {
              "name": "SPINLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "RobustScanner":
          postprocess_params = {
              "name": "SARLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
              "rm_symbol": True,
          }
      elif self.rec_algorithm == "RFL":
          postprocess_params = {
              "name": "RFLLabelDecode",
              "character_dict_path": None,
              "use_space_char": args.use_space_char,
          }
      elif self.rec_algorithm == "SATRN":
          postprocess_params = {
              "name": "SATRNLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
              "rm_symbol": True,
          }
      elif self.rec_algorithm == "PREN":
          postprocess_params = {"name": "PRENLabelDecode"}
      elif self.rec_algorithm == "CAN":
          self.inverse = args.rec_image_inverse
          postprocess_params = {
              "name": "CANLabelDecode",
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char,
          }
      self.postprocess_op = build_post_process(postprocess_params)
      (
          self.predictor,
          self.input_tensor,
          self.output_tensors,
          self.config,
      ) = utility.create_predictor(args, "rec", logger)
      self.benchmark = args.benchmark
      self.use_onnx = args.use_onnx
      if args.benchmark:
          import auto_log
  
          pid = os.getpid()
          gpu_id = utility.get_infer_gpuid()
          self.autolog = auto_log.AutoLogger(
              model_name="rec",
              model_precision=args.precision,
              batch_size=args.rec_batch_num,
              data_shape="dynamic",
              save_path=None,  # args.save_log_path,
              inference_config=self.config,
              pids=pid,
              process_name=None,
              gpu_ids=gpu_id if args.use_gpu else None,
              time_keys=[
                  "preprocess_time",
                  "inference_time",
                  "postprocess_time",
              ],
              warmup=0,
              logger=logger,
          )
  
imports: import os
  import sys
  from PIL import Image
  import cv2
  import numpy
  import math
  import time
  import traceback
  import paddle
  import tools.infer.utility
  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
label_desc: Use image node_id 1 to create a new TextRecognizer object from inherited base classes: object with example: obj = TextRecognizer(args)