code: def setup_callbacks(
      self,
      optimizer,
      callbacks=None,
  ):
      cbs = super().setup_callbacks(optimizer, callbacks)
      if self._retrain_on_original:
          for i, cb in enumerate(cbs[:]):
              if isinstance(cb, TensorBoardCallback):
                  cb.log_dir = os.path.join(cb.log_dir, "aug_data")
  
      return cbs
  
imports: import logging
  import os
  from typing import Type
  from tfaip.data.pipeline.datapipeline import RawDataPipeline
  from tfaip.data.pipeline.definitions import PipelineMode
  from tfaip.trainer.callbacks.logger_callback import LoggerCallback
  from tfaip.trainer.callbacks.tensor_board_callback import TensorBoardCallback
  from tfaip.trainer.callbacks.train_params_logger import TrainerCheckpointsCallback
  from tfaip.trainer.trainer import Trainer
  from tfaip.trainer.warmstart.warmstarter import WarmStarter
  from calamari_ocr.ocr import Codec, SavedCalamariModel
  from calamari_ocr.ocr.dataset.data import Data
  from calamari_ocr.ocr.dataset.imageprocessors.augmentation import AugmentationProcessorParams
  from calamari_ocr.ocr.model.params import ModelParams
  from calamari_ocr.ocr.training.params import TrainerParams
  from calamari_ocr.ocr.training.pipeline_params import CalamariTrainOnlyPipelineParams
  from calamari_ocr.ocr.training.warmstart import WarmStarterWithCodecAdaption
  from calamari_ocr.utils import checkpoint_path
label_desc: Use image node_id 6 for calling the Trainer obj's underlying member method code with example usage: obj.setup_callbacks(optimizer, callbacks) and returns: cbs