code: def __init__(
      self,
      dataset_root_path="/root/data/mvtec",
      class_name="bottle",
      is_train=True,
      resize=[256, 256],
      cropsize=[224, 224],
      is_predict=False,
  ):
      assert (
          class_name in CLASS_NAMES
      ), "class_name: {}, should be in {}".format(
          class_name, CLASS_NAMES
      )
      self.dataset_root_path = dataset_root_path
      self.class_name = class_name
      self.is_train = is_train
      self.resize = resize
      self.cropsize = cropsize
  
      # load dataset
      if not is_predict:
          self.x, self.y, self.mask = self.load_dataset_folder()
  
      # set transforms
      self.transform_x = T.Compose(
          [
              T.Resize(resize),
              T.CenterCrop(cropsize),
              T.ToTensor(),
              T.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
      self.transform_mask = T.Compose(
          [T.Resize(resize), T.CenterCrop(cropsize), T.ToTensor()]
      )
  
imports: import os
  import cv2
  import numpy
  from PIL import Image
  import paddle
  from paddle.io import Dataset
  from paddle.vision import transforms
  from qinspector.cvlib.workspace import register
label_desc: Use image node_id 1 to create a new MVTecDataset object from inherited base classes: Dataset with example: obj = MVTecDataset(dataset_root_path, class_name, is_train, resize, cropsize, is_predict)