code: def _decompress(state: LowPrecisionState, grad: torch.Tensor):
      """
      Casts gradients back to full parameter precision so that further computation happens in full precision.
      """
      orig_grad_data = grad.data
      grad.data = grad.data.to(state.parameter_type)
      # Don't let this memory get reused until after the transfer.
      orig_grad_data.record_stream(torch.cuda.current_stream())
  
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 1 for calling a global function with example usage: _decompress(state, grad) without return types