code: def __lr_finder_dump_params(trainer: "pl.Trainer") -> Dict[str, Any]:
      return {
          "optimizers": trainer.strategy.optimizers,
          "lr_scheduler_configs": trainer.strategy.lr_scheduler_configs,
          "callbacks": trainer.callbacks,
          "loggers": trainer.loggers,
          "max_steps": trainer.fit_loop.max_steps,
          "limit_val_batches": trainer.limit_val_batches,
          "loop_state_dict": deepcopy(trainer.fit_loop.state_dict()),
      }
  
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 3 for calling a global function with example usage: __lr_finder_dump_params(trainer) and returns: dict