code: def __init__(self, model_path, gpu_id=0):
      from models import build_model
      from data_loader import get_dataloader
      from post_processing import get_post_processing
      from utils import get_metric
  
      self.gpu_id = gpu_id
      if (
          self.gpu_id is not None
          and isinstance(self.gpu_id, int)
          and paddle.device.is_compiled_with_cuda()
      ):
          paddle.device.set_device("gpu:{}".format(self.gpu_id))
      else:
          paddle.device.set_device("cpu")
      checkpoint = paddle.load(model_path)
      config = checkpoint["config"]
      config["arch"]["backbone"]["pretrained"] = False
  
      self.validate_loader = get_dataloader(
          config["dataset"]["validate"], config["distributed"]
      )
  
      self.model = build_model(config["arch"])
      self.model.set_state_dict(checkpoint["state_dict"])
  
      self.post_process = get_post_processing(config["post_processing"])
      self.metric_cls = get_metric(config["metric"])
  
file_path: PaddleOCR/benchmark/PaddleOCR_DBNet/tools/eval.py
imports: import os
  import sys
  import pathlib
  import argparse
  import time
  import paddle
  from tqdm.auto import tqdm
label_desc: Use image node_id 1 to create a new EVAL object  with example: obj = EVAL(model_path, gpu_id)