class_docstr: Implements SGDW algorithm.
  
  It has been proposed in `Decoupled Weight Decay Regularization`__.
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      momentum: momentum factor (default: 0)
      weight_decay: weight decay (L2 penalty) (default: 0)
      dampening: dampening for momentum (default: 0)
      nesterov: enables Nesterov momentum (default: False)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.SGDW(model.parameters(), lr=0.1, momentum=0.9)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1711.05101
  
  Note:
      Reference code: https://github.com/pytorch/pytorch/pull/22466
code: def __setstate__(self, state: State) -> None:
      super(SGDW, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("nesterov", False)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from .types import OptFloat, OptLossClosure, Params, State
label_desc: Use image node_id 2 for calling the SGDW obj's underlying member method code with example usage: obj.__setstate__(state) without return types