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: import os
  import warnings
  from contextlib import contextmanager
  import torch
  from horovod.common.util import split_list
  from horovod.torch.compression import Compression
  from horovod.torch.functions import broadcast_object
  from horovod.torch.mpi_ops import allreduce_async_, grouped_allreduce_async_, sparse_allreduce_async
  from horovod.torch.mpi_ops import synchronize
  from horovod.torch.mpi_ops import size
  from horovod.torch.mpi_ops import Average, Adasum, Sum
  from horovod.torch.mpi_ops import rocm_built
  from horovod.torch.mpi_ops import ProcessSet, global_process_set
label_desc: Use image node_id 3 for calling the _DistributedAdasumOptimizer obj's underlying member method code with example usage: obj._register_hooks() without return types