code: def __init__(self, trainer: "pl.Trainer") -> None:
      self.trainer = trainer
      self._ckpt_path: Optional[_PATH] = None
      # flag to know if the user is changing the checkpoint path statefully. See `trainer.ckpt_path.setter`
      self._user_managed: bool = False
      self._loaded_checkpoint: Dict[str, Any] = {}
  
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 1 to create a new _CheckpointConnector object  with example: obj = _CheckpointConnector(trainer)