class_docstr: Implements stochastic gradient descent (optionally with momentum) with weight decay.
  
  Implementation from the paper `Fixing Weight Decay Regularization in Adam`_.
  Nesterov momentum is based on the formula from
  `On the importance of initialization and momentum in deep learning`__.
  
  Args:
  ----
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float): learning rate
      momentum (float, optional): momentum factor (default: 0)
      weight_decay (float, optional): weight decay factor (default: 0)
      dampening (float, optional): dampening for momentum (default: 0)
      nesterov (bool, optional): enables Nesterov momentum (default: False)
  
  .. _Fixing Weight Decay Regularization in Adam:
      https://arxiv.org/abs/1711.05101
  
  Example:
  -------
      >>> optimizer = torch.optim.SGDW(model.parameters(), lr=0.1, momentum=0.9,
                                       weight_decay=1e-5)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
  
  .. note::
      The implementation of SGD with Momentum/Nesterov subtly differs from
      Sutskever et. al. and implementations in some other frameworks.
  
      Considering the specific case of Momentum, the update can be written as
  
      .. math::
                v = \rho * v + g \\
                p = p - lr * v
  
      where p, g, v and :math:`\rho` denote the parameters, gradient,
      velocity, and momentum respectively.
  
      This is in contrast to Sutskever et. al. and
      other frameworks which employ an update of the form
  
      .. math::
           v = \rho * v + lr * g \\
           p = p - v
  
      The Nesterov version is analogously modified.
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code: def __setstate__(self, state):
      super().__setstate__(state)
      for group in self.param_groups:
          group.setdefault("nesterov", False)
  
imports: import logging
  import torch
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau, _LRScheduler
  from torch.optim.optimizer import required
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