code: def _try_to_synchronize(self, p):
      handle, ctx = self._handles[p]
      if poll(handle):
          output = synchronize(handle)
          self._push_pull_delay[p] = self.backward_passes_per_step
          if self._is_tensor_instance:
              fp16_p = self._fp32_to_fp16_map.get(p.__hash__())
          else:
              fp16_p = self._fp32_to_fp16_map.get(p)
          fp16_p.grad.set_(self._compression.decompress(output, ctx))
          p.grad.data.copy_(fp16_p.grad.data)
          p.grad.data = p.grad.data / (self.loss_scale * size())
          self._step_one_param(p)
          fp16_p.data.copy_(p.data)
          self._handles.pop(p)
          return True
      else:
          return False
  
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 13 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj._try_to_synchronize(p) and returns: True, False