code: def create_predictor(args, mode, logger):
      if mode == "det":
          model_dir = args.det_model_dir
      elif mode == "cls":
          model_dir = args.cls_model_dir
      elif mode == "rec":
          model_dir = args.rec_model_dir
      elif mode == "table":
          model_dir = args.table_model_dir
      elif mode == "ser":
          model_dir = args.ser_model_dir
      elif mode == "re":
          model_dir = args.re_model_dir
      elif mode == "sr":
          model_dir = args.sr_model_dir
      elif mode == "layout":
          model_dir = args.layout_model_dir
      else:
          model_dir = args.e2e_model_dir
  
      if model_dir is None:
          logger.info(
              "not find {} model file path {}".format(mode, model_dir)
          )
          sys.exit(0)
      if args.use_onnx:
          import onnxruntime as ort
  
          model_file_path = model_dir
          if not os.path.exists(model_file_path):
              raise ValueError(
                  "not find model file path {}".format(model_file_path)
              )
          sess = ort.InferenceSession(model_file_path)
          return sess, sess.get_inputs()[0], None, None
  
      else:
          file_names = ["model", "inference"]
          for file_name in file_names:
              model_file_path = "{}/{}.pdmodel".format(
                  model_dir, file_name
              )
              params_file_path = "{}/{}.pdiparams".format(
                  model_dir, file_name
              )
              if os.path.exists(model_file_path) and os.path.exists(
                  params_file_path
              ):
                  break
          if not os.path.exists(model_file_path):
              raise ValueError(
                  "not find model.pdmodel or inference.pdmodel in {}".format(
                      model_dir
                  )
              )
          if not os.path.exists(params_file_path):
              raise ValueError(
                  "not find model.pdiparams or inference.pdiparams in {}".format(
                      model_dir
                  )
              )
  
          config = inference.Config(model_file_path, params_file_path)
  
          if hasattr(args, "precision"):
              if args.precision == "fp16" and args.use_tensorrt:
                  precision = inference.PrecisionType.Half
              elif args.precision == "int8":
                  precision = inference.PrecisionType.Int8
              else:
                  precision = inference.PrecisionType.Float32
          else:
              precision = inference.PrecisionType.Float32
  
          if args.use_gpu:
              gpu_id = get_infer_gpuid()
              if gpu_id is None:
                  logger.warning(
                      "GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jetson."
                  )
              config.enable_use_gpu(args.gpu_mem, args.gpu_id)
              if args.use_tensorrt:
                  config.enable_tensorrt_engine(
                      workspace_size=1 << 30,
                      precision_mode=precision,
                      max_batch_size=args.max_batch_size,
                      min_subgraph_size=args.min_subgraph_size,  # skip the minmum trt subgraph
                      use_calib_mode=False,
                  )
  
                  # collect shape
                  trt_shape_f = os.path.join(
                      model_dir, f"{mode}_trt_dynamic_shape.txt"
                  )
  
                  if not os.path.exists(trt_shape_f):
                      config.collect_shape_range_info(trt_shape_f)
                      logger.info(
                          f"collect dynamic shape info into : {trt_shape_f}"
                      )
                  try:
                      config.enable_tuned_tensorrt_dynamic_shape(
                          trt_shape_f, True
                      )
                  except Exception as E:
                      logger.info(E)
                      logger.info(
                          "Please keep your paddlepaddle-gpu >= 2.3.0!"
                      )
  
          elif args.use_npu:
              config.enable_custom_device("npu")
          elif args.use_xpu:
              config.enable_xpu(10 * 1024 * 1024)
          else:
              config.disable_gpu()
              if args.enable_mkldnn:
                  # cache 10 different shapes for mkldnn to avoid memory leak
                  config.set_mkldnn_cache_capacity(10)
                  config.enable_mkldnn()
                  if args.precision == "fp16":
                      config.enable_mkldnn_bfloat16()
                  if hasattr(args, "cpu_threads"):
                      config.set_cpu_math_library_num_threads(
                          args.cpu_threads
                      )
                  else:
                      # default cpu threads as 10
                      config.set_cpu_math_library_num_threads(10)
          # enable memory optim
          config.enable_memory_optim()
          config.disable_glog_info()
          config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
          config.delete_pass("matmul_transpose_reshape_fuse_pass")
          if mode == "re":
              config.delete_pass("simplify_with_basic_ops_pass")
          if mode == "table":
              config.delete_pass(
                  "fc_fuse_pass"
              )  # not supported for table
          config.switch_use_feed_fetch_ops(False)
          config.switch_ir_optim(True)
  
          # create predictor
          predictor = inference.create_predictor(config)
          input_names = predictor.get_input_names()
          if mode in ["ser", "re"]:
              input_tensor = []
              for name in input_names:
                  input_tensor.append(predictor.get_input_handle(name))
          else:
              for name in input_names:
                  input_tensor = predictor.get_input_handle(name)
          output_tensors = get_output_tensors(args, mode, predictor)
          return predictor, input_tensor, output_tensors, config
  
imports: import argparse
  import os
  import sys
  import platform
  import cv2
  import numpy
  import paddle
  from PIL import Image, ImageDraw, ImageFont
  import math
  from paddle import inference
  import time
  import random
  from ppocr.utils.logging import get_logger
label_desc: Use image node_id 5 for calling a global function with example usage: create_predictor(args, mode, logger) and returns: sess,  sess,  None,  None, predictor,  input_tensor,  output_tensors,  config