code: def _try_loop_run(
      trainer: "pl.Trainer", params: Dict[str, Any]
  ) -> None:
      loop = trainer.fit_loop
      loop.load_state_dict(deepcopy(params["loop_state_dict"]))
      loop.restarting = False
      loop.run()
  
imports: import importlib
  import logging
  import os
  import uuid
  from copy import deepcopy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch.callbacks import Callback
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.parsing import lightning_hasattr, lightning_setattr
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.types import STEP_OUTPUT, LRScheduler, LRSchedulerConfig
label_desc: Use image node_id 6 for calling a global function with example usage: _try_loop_run(trainer, params) without return types