code: def _register_ddp_comm_hook(
      model: DistributedDataParallel,
      ddp_comm_state: Optional[object] = None,
      ddp_comm_hook: Optional[Callable] = None,
      ddp_comm_wrapper: Optional[Callable] = None,
  ) -> None:
      """Function to register communication hook for DDP model https://pytorch.org/docs/master/ddp_comm_hooks.html.
  
      Args:
          model:
              DDP model
          ddp_comm_state:
              state is passed to the hook and can be used to maintain
              and update any state information that users would like to
              maintain as part of the training process. Examples: error
              feedback in gradient compression, peers to communicate with
              next in GossipGrad etc.
          ddp_comm_hook:
              hook(state: object, bucket: dist._GradBucket) -> torch.futures.Future
  
              This callable function is called once the bucket is ready. The
              hook can perform whatever processing is needed and return
              a Future indicating completion of any async work (ex: allreduce).
              If the hook doesn't perform any communication, it can also
              just return a completed Future. The Future should hold the
              new value of grad bucket's tensors. Once a bucket is ready,
              c10d reducer would call this hook and use the tensors returned
              by the Future and copy grads to individual parameters.
          ddp_comm_wrapper:
              communication hook wrapper to support a communication hook such
              as FP16 compression as wrapper, which could be combined with
              ddp_comm_hook
  
      Examples::
  
          from torch.distributed.algorithms.ddp_comm_hooks import (
              default_hooks as default,
              powerSGD_hook as powerSGD,
              post_localSGD_hook as post_localSGD,
          )
  
          # fp16_compress_hook for compress gradients
          ddp_model = ...
          _register_ddp_comm_hook(
              model=ddp_model,
              ddp_comm_hook=default.fp16_compress_hook,
          )
  
          # powerSGD_hook
          ddp_model = ...
          _register_ddp_comm_hook(
              model=ddp_model,
              ddp_comm_state=powerSGD.PowerSGDState(
                  process_group=None,
                  matrix_approximation_rank=1,
                  start_powerSGD_iter=5000,
              ),
              ddp_comm_hook=powerSGD.powerSGD_hook,
          )
  
          # post_localSGD_hook
          subgroup, _ = torch.distributed.new_subgroups()
          ddp_model = ...
          _register_ddp_comm_hook(
              model=ddp_model,
              state=post_localSGD.PostLocalSGDState(
                  process_group=None,
                  subgroup=subgroup,
                  start_localSGD_iter=1_000,
              ),
              ddp_comm_hook=post_localSGD.post_localSGD_hook,
          )
  
          # fp16_compress_wrapper combined with other communication hook
          ddp_model = ...
          _register_ddp_comm_hook(
              model=ddp_model,
              ddp_comm_state=powerSGD.PowerSGDState(
                  process_group=None,
                  matrix_approximation_rank=1,
                  start_powerSGD_iter=5000,
              ),
              ddp_comm_hook=powerSGD.powerSGD_hook,
              ddp_comm_wrapper=default.fp16_compress_wrapper,
          )
  
      """
      if ddp_comm_hook is None:
          return
      # inform mypy that ddp_comm_hook is callable
      ddp_comm_hook: Callable = ddp_comm_hook
  
      if ddp_comm_wrapper is not None:
          rank_zero_info(
              f"DDP comm wrapper is provided, apply {ddp_comm_wrapper.__qualname__}({ddp_comm_hook.__qualname__})."
          )
          ddp_comm_hook = ddp_comm_wrapper(ddp_comm_hook)
  
      rank_zero_debug(
          f"Registering DDP comm hook: {ddp_comm_hook.__qualname__}."
      )
      model.register_comm_hook(state=ddp_comm_state, hook=ddp_comm_hook)
  
imports: import itertools
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Sized, Union, cast
  import torch
  from torch import Tensor
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.utils.data import DistributedSampler, Sampler
  from typing_extensions import Self
  from lightning.fabric.utilities.distributed import _DatasetSamplerWrapper
  from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info
  from lightning.pytorch.utilities.types import _SizedIterable
label_desc: Use image node_id 3 for calling a global function with example usage: _register_ddp_comm_hook(model, ddp_comm_state, ddp_comm_hook, ddp_comm_wrapper) without return types