text: from typing import Iterable
  
  from torch.optim import AdamW as _AdamW
  from tsts.cfg import CfgNode as CN
  from tsts.core import OPTIMIZERS
  
  __all__ = ["AdamW"]
  
  
  @OPTIMIZERS.register()
  class AdamW(_AdamW):
      """AdamW implementation.
  
      Example
      -------
      .. code-block:: python
  
          OPTIMIZER:
            NAME: "AdamW"
            LR: 0.001
      """
  
      @classmethod
      def from_cfg(cls, params: Iterable, cfg: CN) -> "AdamW":
          lr = cfg.OPTIMIZER.LR
          weight_decay = cfg.OPTIMIZER.WEIGHT_DECAY
          optim = cls(
              params,
              lr,
              weight_decay=weight_decay,
          )
          return optim
  
      @property
      def require_second_step(self) -> bool:
          return False
  