code: def _register_hooks(self):
      for param_group in self.param_groups:
          for p in param_group["params"]:
              if p.requires_grad:
                  p.grad = p.data.new(p.size()).zero_()
                  self._requires_update.add(p)
                  p_tmp = p.expand_as(p)
                  grad_acc = p_tmp.grad_fn.next_functions[0][0]
                  grad_acc.register_hook(self._make_hook(p))
                  self._grad_accs.append(grad_acc)
  
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 4 for calling the _DistributedOptimizer obj's underlying member method code with example usage: obj._register_hooks() without return types