class_docstr: Implements stochastic gradient descent warm (optionally with momentum).
  
  It has been proposed in `Fixing Weight Decay Regularization in Adam <https://arxiv.org/abs/1711.05101>`_.
  
  Nesterov momentum is based on the formula from
  `On the importance of initialization and momentum in deep learning <http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf>`_.
  
  :param params (iterable): iterable of parameters to optimize or dicts defining parameter groups
  :param lr: (float): learning rate
  :param momentum: (float, optional): momentum factor (default: 0)
  :param weight_decay: (float, optional): weight decay (L2 penalty) (default: 0)
  :param dampening: (float, optional): dampening for momentum (default: 0)
  :param nesterov: (bool, optional): enables Nesterov momentum (default: False)
  
  Example:
      >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input_), target).backward()
      >>> optimizer.step()
  
  .. 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.
code: def __init__(
      self,
      params,
      lr=0.003,
      momentum=0,
      dampening=0,
      weight_decay=0,
      nesterov=False,
  ):
      defaults = dict(
          lr=lr,
          momentum=momentum,
          dampening=dampening,
          weight_decay=weight_decay,
          nesterov=nesterov,
      )
      if nesterov and (momentum <= 0 or dampening != 0):
          raise ValueError(
              "Nesterov momentum requires a momentum and zero dampening"
          )
      super(SGD, self).__init__(params, defaults)
  
imports: import torch
  from torch.optim.optimizer import Optimizer
  from torch.optim import SGD
label_desc: Use image node_id 1 to create a new SGDW object from inherited base classes: Optimizer with example: obj = SGDW(params, lr, momentum, dampening, weight_decay, nesterov)