docstr: Performs a single optimization step. Only applicable to SGD.
  Arguments:
      closure (callable, optional): A closure that reevaluates the model
          and returns the loss.
docstr_tok: ["Performs","a","single","optimization","step",".","Only","applicable","to","SGD",".","Arguments",":","closure","(","callable",",","optional",")",":","A","closure","that","reevaluates","the","model","and","returns","the","loss","."]
code: def _step_one_param(self, param, closure=None):
      """Performs a single optimization step. Only applicable to SGD.
      Arguments:
          closure (callable, optional): A closure that reevaluates the model
              and returns the loss.
      """
      loss = None
      if closure is not None:
          loss = closure()
  
      for group in self.param_groups:
          weight_decay = group["weight_decay"]
          momentum = group["momentum"]
          dampening = group["dampening"]
          nesterov = group["nesterov"]
  
          for p in group["params"]:
              if p.__hash__() != param.__hash__():
                  continue
              if p.grad is None:
                  continue
              d_p = p.grad.data
              if weight_decay != 0:
                  d_p.add_(weight_decay, p.data)
              if momentum != 0:
                  param_state = self.state[p]
                  if "momentum_buffer" not in param_state:
                      buf = param_state[
                          "momentum_buffer"
                      ] = torch.clone(d_p).detach()
                  else:
                      buf = param_state["momentum_buffer"]
                      buf.mul_(momentum).add_(1 - dampening, d_p)
                  if nesterov:
                      d_p = d_p.add(momentum, buf)
                  else:
                      d_p = buf
  
              p.data.add_(-group["lr"], d_p)
  
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from byteps.torch import _DistributedOptimizer
  from byteps.torch.compression import Compression
  from byteps.torch.ops import push_pull_async_inplace
  from byteps.torch.ops import push_pull
  from byteps.torch.ops import poll, synchronize, declare
  from byteps.torch.ops import init, shutdown
  from byteps.torch.ops import size, local_size, rank, local_rank
  import time
  import threading
  import torch
  import collections
label_desc: Use image node_id 10 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj._step_one_param(param, closure) and returns: loss