code: def __init__(
      self,
      dataset_root="data/TemDataSet",
      raw_dataset_dir="TemDataSet_seg_raw/",
      images_dir="train_imgs",
      labels_dir="train_labels",
      phase_dir="phase0",
      urls=None,
      valid_suffix=("nii.gz", "nii.gz"),
      filter_key=(None, None),
      uncompress_params={"format": "zip", "num_files": 1},
      images_dir_test="",
  ):
      super().__init__(
          dataset_root,
          raw_dataset_dir,
          images_dir,
          labels_dir,
          phase_dir,
          urls,
          valid_suffix,
          filter_key,
          uncompress_params,
          images_dir_test,
      )
  
      self.preprocess = {
          "images": [  # todo: make params set automatically
              normalize,
              wrapped_partial(
                  resample, new_shape=[512, 512, 24], order=1
              ),
          ],
          "labels": [
              wrapped_partial(
                  resample, new_shape=[512, 512, 24], order=0
              )
          ],
          "images_test": [
              normalize,
          ],
      }
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/prepare_prostate.py
imports: import os
  import sys
  import zipfile
  import functools
  import numpy
  from prepare import Prep
  from tools.preprocess_utils import resample, normalize, label_remap, join_paths
  from medicalseg.utils import wrapped_partial
label_desc: Use image node_id 1 to create a new Prep_prostate object from inherited base classes: Prep with example: obj = Prep_prostate(dataset_root, raw_dataset_dir, images_dir, labels_dir, phase_dir, urls, valid_suffix, filter_key, uncompress_params, images_dir_test)