code: def __lr_finder_reset_params(
      trainer: "pl.Trainer",
      num_training: int,
      early_stop_threshold: Optional[float],
  ) -> None:
      from lightning.pytorch.loggers.logger import DummyLogger
  
      trainer.strategy.lr_scheduler_configs = []
      # Use special lr logger callback
      trainer.callbacks = [
          _LRCallback(
              num_training,
              early_stop_threshold,
              progress_bar_refresh_rate=1,
          )
      ]
      # No logging
      trainer.logger = (
          DummyLogger() if trainer.logger is not None else None
      )
      # Max step set to number of iterations starting at current number of iterations
      trainer.fit_loop.epoch_loop.max_steps = (
          num_training + trainer.global_step
      )
      trainer.limit_val_batches = num_training
  
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 4 for calling a global function with example usage: __lr_finder_reset_params(trainer, num_training, early_stop_threshold) without return types