code: def _push_pull_grad_async(self, p):
      if self._is_tensor_instance:
          name = self._parameter_names.get(p.__hash__())
      else:
          name = self._parameter_names.get(p)
      if self._enable_async:
          # the real handle will be created in step()
          handle, ctx = None, None
      else:
          tensor = p.grad
          tensor_compressed, ctx = self._compression.compress(tensor)
          handle = byteps_push_pull(
              tensor_compressed, average=True, name="Gradient." + name
          )
      return handle, ctx
  
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 5 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj._push_pull_grad_async(p) and returns: handle,  ctx