code: def safe_set_full_optimizer_state(param, value, optim_state_key):
      """Update the partitioned fp32 optimizer state of a low-precision (e.g., fp16) parameter.
  
      Args:
          param (``torch.nn.Parameter``): A model parameter
          value (``torch.Tensor``): New value
          optim_state_key (``string``): Key value of optimizer state (e.g., `exp_avg` in Adam optimizer)
      """
      # ZeRO stage 3 param
      if hasattr(param, "ds_id"):
          param._z3_optimizer.set_full_hp_param(
              value, param, optim_state_key
          )
  
      # ZeRO stage 1, 2, and bf16_optimizer params
      if hasattr(param, "_hp_mapping"):
          param.set_full_hp_param(value, optim_state_key)
  
imports: import torch
  from dataclasses import dataclass
  from deepspeed import comm
  from typing import Dict
label_desc: Use image node_id 7 for calling a global function with example usage: safe_set_full_optimizer_state(param, value, optim_state_key) without return types