code: def get_state_for_inference(
      self, save_path: str, device: Optional[torch.device] = None
  ) -> Mapping[str, Any]:
      if self.model.zero_optimization_stage() == 3:
          return get_fp32_state_dict_from_zero_checkpoint(save_path)
  
      self.model.load_checkpoint(
          save_path,
          load_optimizer_states=False,
          load_lr_scheduler_states=False,
          load_module_only=True,
      )
      return self.model.module.cpu().state_dict()
  
imports: import logging
  import os
  import warnings
  from typing import Any, Dict, List, Mapping, Optional, Tuple, TYPE_CHECKING, Union
  import deepspeed
  import deepspeed.comm
  import torch
  from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
  from packaging import version
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from ludwig.constants import MIN_POSSIBLE_BATCH_SIZE
  from ludwig.distributed.ddp import DDPStrategy
  from ludwig.modules.optimization_modules import get_optimizer_class_and_kwargs
  from ludwig.utils.checkpoint_utils import Checkpoint
  from ludwig.utils.model_utils import extract_tensors, replace_tensors
label_desc: Use image node_id 4 for calling the DeepSpeedCheckpoint obj's underlying member method code with example usage: obj.get_state_for_inference(save_path, device) and returns: self, get_fp32_state_dict_from_zero_checkpoint