class_docstr: FlashDeepSpeedFinetuning can be used to create a custom Flash Finetuning Callback which works with DeepSpeed.
  
  DeepSpeed cannot store and load its parameters when working with Lightning. So FlashDeepSpeedFinetuning overrides
  `_store` to not store its parameters.
class_docstr_tok: ["FlashDeepSpeedFinetuning","can","be","used","to","create","a","custom","Flash","Finetuning","Callback","which","works","with","DeepSpeed",".","DeepSpeed","can","not","store","and","load","its","parameters","when","working","with","Lightning",".","So","FlashDeepSpeedFinetuning","overrides","`","_store","`","to","not","store","its","parameters","."]
code: def _store(
      self,
      pl_module: LightningModule,
      opt_idx: int,
      num_param_groups: int,
      current_param_groups: List[Dict[str, Any]],
  ) -> None:
      pass
  
imports: import os
  from functools import partial
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  from pytorch_lightning import LightningModule
  from pytorch_lightning.callbacks import BaseFinetuning
  from torch.nn import Module
  from torch.optim import Optimizer
  from flash.core.registry import FlashRegistry
label_desc: Use image node_id 1 for calling the FlashDeepSpeedFinetuning obj's underlying member method code with example usage: obj._store(pl_module, opt_idx, num_param_groups, current_param_groups) without return types