code: def should_sync_gradnorm(opt):
      """
      Indicates whether fp16 optimizer wrappers should accumulate over workers.
  
      FP16 overflow detection and gradient clipping both require accumulating gradients
      across all workers when using FSDP, as workers only store a fraction of the
      gradients.
      """
      return (
          FSDP_AVAILABLE
          and opt["fp16"]
          and opt.get("ddp_backend", DEFAULT_DDP_BACKEND)
          in ("zero2", "zero3")
      )
  
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 5 for calling a global function with example usage: should_sync_gradnorm(opt) and returns: unknown