code: def _restore_modules_and_callbacks(
      self, checkpoint_path: Optional[_PATH] = None
  ) -> None:
      # restore modules after setup
      self.resume_start(checkpoint_path)
      self.restore_model()
      self.restore_datamodule()
      if self.trainer.state.fn == TrainerFn.FITTING:
          # restore callback states
          self.restore_callbacks()
  
imports: import logging
  import os
  import re
  from typing import Any, Dict, Optional
  import torch
  from fsspec.core import url_to_fs
  from fsspec.implementations.local import LocalFileSystem
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.plugins.environments.slurm import SLURMEnvironment
  from lightning.fabric.utilities.cloud_io import _is_dir, get_filesystem
  from lightning.fabric.utilities.types import _PATH
  from lightning.pytorch.callbacks import ModelCheckpoint
  from lightning.pytorch.plugins.precision import MixedPrecision
  from lightning.pytorch.trainer import call
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
  from lightning.pytorch.utilities.migration import pl_legacy_patch
  from lightning.pytorch.utilities.migration.utils import _pl_migrate_checkpoint
  from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
label_desc: Use image node_id 17 for calling the _CheckpointConnector obj's underlying member method code with example usage: obj._restore_modules_and_callbacks(checkpoint_path) without return types