code: def synchronize(self):
      missing_p = self._requires_update - set(self._handles.keys())
      for p in missing_p:
          handle, ctx = self._push_pull_grad_async(p)
          self._handles[p] = (handle, ctx)
  
      for p, value in self._handles.items():
          handle, ctx = value
          if handle is None:
              handle, ctx = self._push_pull_grad_async(p)
              self._handles[p] = (handle, ctx)
      for p, (handle, ctx) in self._handles.items():
          output = synchronize(handle)
          self._push_pull_delay[p] = self.backward_passes_per_step
          if not self._enable_async:
              tmp = self._compression.decompress(output, ctx)
              try:
                  p.grad.set_(tmp)
              except Exception as e:
                  if (
                      "set_storage is not allowed on a Tensor created from .data or .detach()."
                      in str(e)
                  ):
                      p.grad.copy_(tmp)
                  else:
                      raise
      self._handles.clear()
  
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 7 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj.synchronize() without return types