code: def reduce_scatter_hook(
      state: DefaultState, grad: torch.Tensor, output: torch.Tensor
  ):
      r"""
      Implement the  FSDP communication hook for ``reduce_scatter`` algorithm.
  
      For sharded FSDP strategies and a necessary pre- and post-division of gradients.
  
      Args:
          state (DefaultState): State information, configures pre- and post-division factors.
          grad (torch.Tensor): An unsharded gradient for the local batch that needs to be
          communicated across ranks.
          output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
      """
      # Average grad by pre-division factor.
      if state.gradient_predivide_factor > 1:
          grad.div_(state.gradient_predivide_factor)
      dist.reduce_scatter_tensor(
          output, grad, group=state.process_group
      )
      # Average grad's shard by post-division factor.
      if state.gradient_postdivide_factor > 1:
          output.div_(state.gradient_postdivide_factor)
  
file_path: pytorch/torch/distributed/algorithms/_comm_hooks/default_hooks.py
imports: import functools
  import torch
  import torch.distributed
  from typing import Optional
label_desc: Use image node_id 3 for calling a global function with example usage: reduce_scatter_hook(state, grad, output) without return types