class_docstr: Implements stochastic gradient descent (optionally with momentum).
  
  Currently GPU-only.  Requires Apex to be installed via
  ``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
  
  This version of fused SGD implements 2 fusions.
  
    * Fusion of the SGD update's elementwise operations
    * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
  
  :class:`apex.optimizers.FusedSGD` may be used as a drop-in replacement for ``torch.optim.SGD``::
  
      opt = apex.optimizers.FusedSGD(model.parameters(), lr = ....)
      ...
      opt.step()
  
  :class:`apex.optimizers.FusedSGD` may be used with or without Amp.  If you wish to use :class:`FusedSGD` with Amp,
  you may choose any ``opt_level``::
  
      opt = apex.optimizers.FusedSGD(model.parameters(), lr = ....)
      model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2")
      ...
      opt.step()
  
  In general, ``opt_level="O1"`` is recommended.
  
  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 (L2 penalty) (default: 0)
      dampening (float, optional): dampening for momentum (default: 0)
      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()
  
  __ 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.
code: def zero_grad(self):
      if self.set_grad_none:
          for group in self.param_groups:
              for p in group["params"]:
                  p.grad = None
      else:
          super(FusedSGD, self).zero_grad()
  
file_path: apex/apex/optimizers/fused_sgd.py
imports: import torch
  from torch.optim.optimizer import Optimizer, required
  from apex.multi_tensor_apply import multi_tensor_applier
label_desc: Use image node_id 3 for calling the FusedSGD obj's underlying member method code with example usage: obj.zero_grad() without return types