code: def bf16_compress_hook(
      state: LowPrecisionState,
      grad: torch.Tensor,
      output: Optional[torch.Tensor] = None,
  ):
      r"""
      Implement FSDP communication hook for a simple gradient compression approach .
      Casts ``grad`` to half-precision floating-point format.
  
      It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a
      ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``)
      gradients are averaged by a ``state.gradient_postdivide_factor``.
      Once post-division is done, compressed gradients are casted back to parameters' precision.
  
      Args:
          state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision.
          grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision.
          output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
      """
      bf16_hook = functools.partial(_low_precision_hook, torch.bfloat16)
      return bf16_hook(state, grad, output)
  
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 6 for calling a global function with example usage: bf16_compress_hook(state, grad, output) and returns: bf16_hook