code: def _configure_checkpoint_callbacks(
      self, enable_checkpointing: bool
  ) -> None:
      if self.trainer.checkpoint_callbacks:
          if not enable_checkpointing:
              raise MisconfigurationException(
                  "Trainer was configured with `enable_checkpointing=False`"
                  " but found `ModelCheckpoint` in callbacks list."
              )
      elif enable_checkpointing:
          self.trainer.callbacks.append(ModelCheckpoint())
  
imports: import logging
  import os
  from datetime import timedelta
  from typing import Dict, List, Optional, Sequence, Union
  import lightning.pytorch
  from lightning.fabric.utilities.registry import _load_external_callbacks
  from lightning.pytorch.callbacks import Callback, Checkpoint, ModelCheckpoint, ModelSummary, ProgressBar, RichProgressBar, TQDMProgressBar
  from lightning.pytorch.callbacks.batch_size_finder import BatchSizeFinder
  from lightning.pytorch.callbacks.lr_finder import LearningRateFinder
  from lightning.pytorch.callbacks.rich_model_summary import RichModelSummary
  from lightning.pytorch.callbacks.timer import Timer
  from lightning.pytorch.trainer import call
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import rank_zero_info
label_desc: Use image node_id 3 for calling the _CallbackConnector obj's underlying member method code with example usage: obj._configure_checkpoint_callbacks(enable_checkpointing) without return types