code: def step(self, closure=None, wait_for_finish=True):
      if size() > 1:
          self._sync_missing_gradients()
          if wait_for_finish:
              self._wait_for_all()
          loss = None
          if closure is not None:
              loss = closure()
          return loss
      else:
          super(self.__class__, self).step()
  
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 9 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj.step(closure, wait_for_finish) and returns: loss