code: def _register_backward_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)
                  if self._is_tensor_instance:
                      fp16_p = self._fp32_to_fp16_map.get(p.__hash__())
                  else:
                      fp16_p = self._fp32_to_fp16_map.get(p)
                  p_tmp = fp16_p.expand_as(fp16_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 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 5 for calling the _HalfPrecisionDistributedOptimizer obj's underlying member method code with example usage: obj._register_backward_hooks() without return types