code: def clean_raw_data(self):
      maybe_mkdir_p(join(self.clean_folder, "imagesTr"))
      maybe_mkdir_p(join(self.clean_folder, "labelsTr"))
  
      # train
      all_train_files = []
      patient_dirs_train = subfolders(self.folder, prefix="patient")
      for p in patient_dirs_train:
          current_dir = p
          data_files_train = [
              i
              for i in subfiles(current_dir, suffix=".nii.gz")
              if i.find("_gt") == -1 and i.find("_4d") == -1
          ]
          corresponding_seg_files = [
              i[:-7] + "_gt.nii.gz" for i in data_files_train
          ]
          for d, s in zip(data_files_train, corresponding_seg_files):
              patient_identifier = os.path.split(d)[1][:-7]
              all_train_files.append(
                  patient_identifier + "_0000.nii.gz"
              )
              shutil.copy(
                  d,
                  join(
                      self.clean_folder,
                      "imagesTr",
                      patient_identifier + "_0000.nii.gz",
                  ),
              )
              shutil.copy(
                  s,
                  join(
                      self.clean_folder,
                      "labelsTr",
                      patient_identifier + "_0000.nii.gz",
                  ),
              )
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/prepare_acdc.py
imports: import nibabel
  import shutil
  import os.path
  from preprocess_utils.file_and_folder_operations import *
  from preprocess_utils.geometry import *
  from preprocess_utils.path_utils import join_paths
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling the PrepACDC obj's underlying member method code with example usage: obj.clean_raw_data() without return types