code: def delay_halving(opt):
      """
      Check whether we should keep the model in fp32 before other setup.
  
      When using Zero2 or Zero3 backends with mixed precision, we need to avoid converting
      the model to fp16, as the FSDP module does this for us.
  
      If we are using just plain DDP or MemoryEfficient optimizers, then we want
      to call half() early.
      """
  
      return (
          opt["fp16"]
          and should_use_fsdp(opt)
          and opt["fp16_impl"] == "safe"
      )
  
imports: import contextlib
  import functools
  import torch
  import torch.distributed
  from torch.distributed.algorithms.join import Join, Joinable, JoinHook
  import torch.nn
  from parlai.scripts.eval_model import Evaluator
  from parlai.scripts.train_model import TrainLoop
  from parlai.utils.distributed import is_distributed, get_dist_group
label_desc: Use image node_id 4 for calling a global function with example usage: delay_halving(opt) and returns: unknown