text: dataset_type = 'UnconditionalImageDataset'
  
  train_pipeline = [
      dict(
          type='LoadImageFromFile',
          key='real_img',
          io_backend='disk',
      ),
      dict(type='Flip', keys=['real_img'], direction='horizontal'),
      dict(type='Normalize',
           keys=['real_img'],
           mean=[127.5] * 3,
           std=[127.5] * 3,
           to_rgb=True),
      dict(type='ImageToTensor', keys=['real_img']),
      dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])
  ]
  
  val_pipeline = [
      dict(
          type='LoadImageFromFile',
          key='real_img',
          io_backend='disk',
      ),
      dict(type='Normalize',
           keys=['real_img'],
           mean=[127.5] * 3,
           std=[127.5] * 3,
           to_rgb=True),
      dict(type='ImageToTensor', keys=['real_img']),
      dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])
  ]
  
  # `samples_per_gpu` and `imgs_root` need to be set.
  data = dict(samples_per_gpu=None,
              workers_per_gpu=4,
              train=dict(type='RepeatDataset',
                         times=100,
                         dataset=dict(type=dataset_type,
                                      imgs_root=None,
                                      pipeline=train_pipeline)),
              val=dict(type=dataset_type, imgs_root=None, pipeline=val_pipeline))
  