code: def prepare_for_backward(
      model: DistributedDataParallel, output: Any
  ) -> None:
      # `prepare_for_backward` is `DistributedDataParallel` specific.
      if torch.is_grad_enabled() and model.require_backward_grad_sync:
          model.require_forward_param_sync = True
          # We'll return the output object verbatim since it is a freeform
          # object. We need to find any tensors in this object, though,
          # because we need to figure out which parameters were used during
          # this forward pass, to ensure we short circuit reduction for any
          # unused parameters. Only if `find_unused_parameters` is set.
          args = (
              list(_find_tensors(output))
              if model.find_unused_parameters and not model.static_graph
              else []
          )
          reducer = cast(
              torch._C._distributed_c10d.Reducer, model.reducer
          )
          reducer._rebuild_buckets()  # avoids "INTERNAL ASSERT FAILED" with `find_unused_parameters=False`
          reducer.prepare_for_backward(args)
      else:
          model.require_forward_param_sync = False
  
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 2 for calling a global function with example usage: prepare_for_backward(model, output) without return types