code: def __init__(self):
      super().__init__(
          dataset_root="data/abdomen",
          raw_dataset_dir="abdomen_raw/",
          images_dir="RawData/RawData/Training/img",
          labels_dir="RawData/RawData/Training/label",
          phase_dir="abdomen_phase0/",
          urls=urls,
          valid_suffix=("nii.gz", "nii.gz"),
          filter_key=(None, None),
          uncompress_params={"format": "zip", "num_files": 1},
      )
  
      self.preprocess = {
          "images": [
              wrapped_partial(np.clip, a_min=-125, a_max=275),
              wrapped_partial(
                  HUnorm, HU_min=-125, HU_max=275, multiply_255=False
              ),
          ],
          "labels": [
              wrapped_partial(ignore_label, label_map=label_map)
          ],
      }
      self.train_image_files = []
      self.val_image_files = []
      self.train_label_files = []
      self.val_label_files = []
  
      self.train_image_files_npy = []
      self.val_image_files_npy = []
      self.train_label_files_npy = []
      self.val_label_files_npy = []
      self.train_val_split()
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/prepare_abdomen.py
imports: import os
  import sys
  import os.path
  import time
  import json
  import numpy
  from tqdm import tqdm
  from prepare import Prep
  from preprocess_utils import HUnorm, ignore_label, join_paths
  from medicalseg.utils import wrapped_partial
label_desc: Use image node_id 1 to create a new Prep_abdomen object from inherited base classes: Prep with example: obj = Prep_abdomen()