class_docstr: A base class for offloading optimizer states.
  
  Args:
      params: parameters
      defaults (dict): default dict
      nvme_offload_fraction (float, optional): Fraction of params to be offloaded to NVMe. Defaults to 0.0.
      offload_dir (Optional[str], optional): Directory to save NVMe offload files.
          If it's ``None``, a random temporary directory will be used. Defaults to None.
  
  Raises:
      ImportError: Raise if ``tensornvme`` is not installed.
class_docstr_tok: ["A","base","class","for","offloading","optimizer","states",".","Args",":","params",":","parameters","defaults","(","dict",")",":","default","dict","nvme_offload_fraction","(","float",",","optional",")",":","Fraction","of","params","to","be","offloaded","to","NVMe",".","Defaults","to","0.0.","offload_dir","(","Optional","[","str","]",",","optional",")",":","Directory","to","save","NVMe","offload","files",".","If","it","'s","``","None","``",",","a","random","temporary","directory","will","be","used",".","Defaults","to","None",".","Raises",":","ImportError",":","Raise","if","``","tensornvme","``","is","not","installed","."]
code: def load_state_dict(self, state_dict: dict) -> None:
      # TODO(ver217): design a new method to load state_dict. When using NVMe offload, whole state_dict may not be able to fit in memory.
      if self.offloader is not None:
          raise NotImplementedError
      super().load_state_dict(state_dict)
  
imports: import math
  import os
  import tempfile
  from typing import Callable, Dict, List, Optional
  import torch
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 11 for calling the NVMeOptimizer obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types