code: def main():
      args = argsparser()
      config_parser = ConfigParser(args)
      args = config_parser.parser()
  
      random.seed(args.seed)
      np.random.seed(args.seed)
      paddle.seed(args.seed)
      paddle.device.set_device(args.device)
  
      class_name = args.category
      assert class_name in mvtec.CLASS_NAMES
      print("Testing model for {}".format(class_name))
      # build model
      model = get_model(args.method)(
          arch=args.backbone,
          pretrained=False,
          k=args.k,
          method=args.method,
      )
      model.eval()
      state = paddle.load(args.model_path)
      model.model.set_dict(state["params"])
      model.load(state["stats"])
      model.eval()
  
      # build data
      MVTecDataset = mvtec.MVTecDataset(is_predict=True)
      transform_x = MVTecDataset.get_transform_x()
      x = Image.open(args.img_path).convert("RGB")
      x = transform_x(x).unsqueeze(0)
      predict(args, model, x)
  
file_path: PaddleSeg/contrib/QualityInspector/tools/uad/patchcore/predict.py
imports: import os
  import sys
  import random
  import argparse
  import datetime
  import numpy
  from PIL import Image
  import paddle
  import qinspector.uad.datasets.mvtec
  from qinspector.uad.models.patchcore import get_model
  from qinspector.uad.utils.utils import plot_fig, str2bool
  from qinspector.cvlib.uad_configs import ConfigParser
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types