code: def maybe_zero_3(param, ignore_status=False, name=None):
      from deepspeed import zero
      from deepspeed.runtime.zero.partition_parameters import (
          ZeroParamStatus,
      )
  
      if hasattr(param, "ds_id"):
          if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
              if not ignore_status:
                  print(name, "no ignore status")
          with zero.GatheredParameters([param]):
              param = param.data.detach().cpu().clone()
      else:
          param = param.detach().cpu().clone()
      return param
  
imports: import os
  import torch
  import torch.nn
  from fairscale.optim import OSS
  from torch.utils.data import Sampler
  from transformers import Trainer
  from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
  from transformers.trainer_pt_utils import get_parameter_names
  from transformers.trainer_utils import ShardedDDPOption, has_length
  from transformers.utils import is_sagemaker_mp_enabled, logging
  from typing import List, Optional, Union, Dict, Any
label_desc: Use image node_id 1 for calling a global function with example usage: maybe_zero_3(param, ignore_status, name) and returns: param