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:
                  logging.warning(
                      f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}"
                  )
          with zero.GatheredParameters([param]):
              param = param.data.detach().cpu().clone()
      else:
          param = param.detach().cpu().clone()
      return param
  
imports: import os
  import copy
  from dataclasses import dataclass, field
  import json
  import logging
  import pathlib
  from typing import Dict, Optional, Sequence, List
  import torch
  import transformers
  from llmga.llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_OUTPUT_START_TOKEN, DEFAULT_OUTPUT_END_TOKEN
  from torch.utils.data import Dataset
  from llmga.llava.train.llava_trainer import LLaVATrainer
  from llmga.llava import conversation
  from llmga.llava.model import *
  from llmga.llava.mm_utils import tokenizer_image_token
  from PIL import Image
  from llmga.llava.masks.make_mask import get_mask_generator
  from llmga.llava.prompt_temp import outpaint_prompt, inpaint_prompt, textextend_prompt, regen_prompt, ques_prompt, textextend_prompt2
  import random
  import numpy
  import lmdb
  import io
  import pytextrank
  from torchvision import transforms
label_desc: Use image node_id 2 for calling a global function with example usage: maybe_zero_3(param, ignore_status, name) and returns: param