code: def _make_hook(self, p):
      def hook(*ignore):
          if p in self._handles and self._handles[p][0] is not None:
              if self._push_pull_delay[p] <= 0:
                  raise AssertionError(
                      "Gradients were computed more than "
                      "backward_passes_per_step times before call "
                      "to step(). Increase backward_passes_per_step to "
                      "accumulate gradients locally."
                  )
          assert not p.grad.requires_grad
          assert self._push_pull_delay[p] > 0
          handle, ctx = None, None
          self._push_pull_delay[p] -= 1
          if self._push_pull_delay[p] == 0:
              handle, ctx = self._push_pull_grad_async(p)
          self._handles[p] = (handle, ctx)
  
      return hook
  
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 6 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj._make_hook(p) and returns: hook