code: def step(self, closure=None):
      if self._enable_async:
          old_weight_map = {}
          # store the weights before update
          for p, _ in self._handles.items():
              old_weight_map[p] = p.data.clone().detach()
          # update
          loss = super(self.__class__, self).step(closure)
  
          for p, (h, _) in self._handles.items():
              # get the diff for each weight (in-place)
              p.data.sub_(old_weight_map.get(p))
              if h is None:
                  # create the handler now
                  if self._is_tensor_instance:
                      name = self._parameter_names.get(p.__hash__())
                  else:
                      name = self._parameter_names.get(p)
                  handle = byteps_push_pull(
                      p, average=False, name="AsyncParam." + name
                  )
                  _, ctx = self._compression.compress(p)
                  self._handles[p] = (handle, ctx)
  
          self.synchronize()
          return loss
      else:
          # skip sync if calling skip_synchronize
          if self._should_sync:
              self.synchronize()
          return super(self.__class__, self).step(closure)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from contextlib import contextmanager
  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, suspend, resume
  from byteps.torch.ops import size, local_size, rank, local_rank
  import os
  import torch
  import collections
  import io
  import cloudpickle
label_desc: Use image node_id 9 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj.step(closure) and returns: loss, super