code: def _push_pull_grad_async(self, p):
      if self._is_tensor_instance:
          name = self._parameter_names.get(p.__hash__())
          fp16_p = self._fp32_to_fp16_map.get(p.__hash__())
      else:
          name = self._parameter_names.get(p)
          fp16_p = self._fp32_to_fp16_map.get(p)
      tensor = fp16_p.grad
      tensor_compressed, ctx = self._compression.compress(tensor)
      if fp16_p not in self.priorities:
          self.priorities[fp16_p] = self.gradient_count
          self.gradient_count += 1
      handle = byteps_push_pull(
          tensor_compressed,
          average=False,
          name="Gradient." + name,
          priority=self.priorities[fp16_p],
      )
      return 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 6 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj._push_pull_grad_async(p) and returns: handle,  ctx