code: def load_predictor(
      model_dir,
      precision="fp32",
      use_trt=False,
      use_mkldnn=False,
      batch_size=1,
      device="CPU",
      min_subgraph_size=3,
      use_dynamic_shape=False,
      trt_min_shape=1,
      trt_max_shape=1280,
      trt_opt_shape=640,
      cpu_threads=1,
  ):
      """set AnalysisConfig, generate AnalysisPredictor
      Args:
          model_dir (str): root path of __model__ and __params__
          precision (str): mode of running(fp32/fp16/int8)
          use_trt (bool): whether use TensorRT or not.
          use_mkldnn (bool): whether use MKLDNN or not in CPU.
          device (str): Choose the device you want to run, it can be: CPU/GPU, default is CPU
          use_dynamic_shape (bool): use dynamic shape or not
          trt_min_shape (int): min shape for dynamic shape in trt
          trt_max_shape (int): max shape for dynamic shape in trt
          trt_opt_shape (int): opt shape for dynamic shape in trt
      Returns:
          predictor (PaddlePredictor): AnalysisPredictor
      Raises:
          ValueError: predict by TensorRT need device == 'GPU'.
      """
      rerun_flag = False
      if device != "GPU" and use_trt:
          raise ValueError(
              "Predict by TensorRT mode: {}, expect device=='GPU', but device == {}".format(
                  precision, device
              )
          )
      config = Config(
          os.path.join(model_dir, "model.pdmodel"),
          os.path.join(model_dir, "model.pdiparams"),
      )
      if device == "GPU":
          # initial GPU memory(M), device ID
          config.enable_use_gpu(200, 0)
          # optimize graph and fuse op
          config.switch_ir_optim(True)
      else:
          config.disable_gpu()
          config.set_cpu_math_library_num_threads(cpu_threads)
          config.switch_ir_optim()
          if use_mkldnn:
              config.enable_mkldnn()
              if precision == "int8":
                  config.enable_mkldnn_int8(
                      {
                          "conv2d",
                          "depthwise_conv2d",
                          "transpose2",
                          "pool2d",
                      }
                  )
  
      precision_map = {
          "int8": Config.Precision.Int8,
          "fp32": Config.Precision.Float32,
          "fp16": Config.Precision.Half,
      }
      if precision in precision_map.keys() and use_trt:
          config.enable_tensorrt_engine(
              workspace_size=(1 << 25) * batch_size,
              max_batch_size=batch_size,
              min_subgraph_size=min_subgraph_size,
              precision_mode=precision_map[precision],
              use_static=True,
              use_calib_mode=False,
          )
  
          if use_dynamic_shape:
              dynamic_shape_file = os.path.join(
                  FLAGS.model_path, "dynamic_shape.txt"
              )
              if os.path.exists(dynamic_shape_file):
                  config.enable_tuned_tensorrt_dynamic_shape(
                      dynamic_shape_file, True
                  )
                  print("trt set dynamic shape done!")
              else:
                  config.collect_shape_range_info(dynamic_shape_file)
                  print("Start collect dynamic shape...")
                  rerun_flag = True
  
      # enable shared memory
      config.enable_memory_optim()
      predictor = create_predictor(config)
      return predictor, rerun_flag
  
imports: import os
  import argparse
  import time
  import sys
  import cv2
  import numpy
  import paddle
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from ppdet.core.workspace import load_config, create
  from ppdet.metrics import COCOMetric
  from post_process import PPYOLOEPostProcess
label_desc: Use image node_id 6 for calling a global function with example usage: load_predictor(model_dir, precision, use_trt, use_mkldnn, batch_size, device, min_subgraph_size, use_dynamic_shape, trt_min_shape, trt_max_shape, trt_opt_shape, cpu_threads) and returns: predictor,  rerun_flag