content: import os
  
  from batchgenerators.utilities.file_and_folder_operations import *
  
  from nnunetv2.paths import nnUNet_raw
  from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets
  
  if __name__ == '__main__':
      # creates a dummy dataset where there are no files in imagestr and labelstr
      source_dataset = 'Dataset004_Hippocampus'
  
      target_dataset = 'Dataset989_dummyDataset4_2'
      target_dataset_dir = join(nnUNet_raw, target_dataset)
      maybe_mkdir_p(target_dataset_dir)
  
      dataset = get_filenames_of_train_images_and_targets(join(nnUNet_raw, source_dataset))
  
      # the returned dataset will have absolute paths. We should use relative paths so that you can freely copy
      # datasets around between systems. As long as the source dataset is there it will continue working even if
      # nnUNet_raw is in different locations
  
      # paths must be relative to target_dataset_dir!!!
      for k in dataset.keys():
          dataset[k]['label'] = join('$nnUNet_raw', os.path.relpath(dataset[k]['label'], nnUNet_raw))
          dataset[k]['images'] = [join('$nnUNet_raw', os.path.relpath(i,  nnUNet_raw)) for i in dataset[k]['images']]
  
      # load old dataset.json
      dataset_json = load_json(join(nnUNet_raw, source_dataset, 'dataset.json'))
      dataset_json['dataset'] = dataset
  
      # save
      save_json(dataset_json, join(target_dataset_dir, 'dataset.json'), sort_keys=False)
  