code: def _reset_progress(trainer: "pl.Trainer") -> None:
      if trainer.lightning_module.automatic_optimization:
          trainer.fit_loop.epoch_loop.automatic_optimization.optim_progress.reset()
      else:
          trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.reset()
  
      trainer.fit_loop.epoch_progress.reset()
  
file_path: pytorch-lightning/src/lightning/pytorch/tuner/batch_size_scaling.py
imports: import logging
  import os
  import uuid
  from copy import deepcopy
  from typing import Any, Dict, Optional, Tuple
  import lightning.pytorch
  from lightning.pytorch.utilities.memory import garbage_collection_cuda, is_oom_error
  from lightning.pytorch.utilities.parsing import lightning_getattr, lightning_setattr
  from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn
label_desc: Use image node_id 10 for calling a global function with example usage: _reset_progress(trainer) without return types