code: def _sync_missing_gradients(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)
  
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 8 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj._sync_missing_gradients() without return types