code: def _get_loops_state_dict(self) -> Dict[str, Any]:
      return {
          "fit_loop": self.trainer.fit_loop.state_dict(),
          "validate_loop": self.trainer.validate_loop.state_dict(),
          "test_loop": self.trainer.test_loop.state_dict(),
          "predict_loop": self.trainer.predict_loop.state_dict(),
      }
  
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 20 for calling the _CheckpointConnector obj's underlying member method code with example usage: obj._get_loops_state_dict() and returns: dict