code: def mark_persistent_parameters(
      self, param_threshold, model_threshold
  ):
      persistent_params = []
      total_persistent_parameters = 0
      params_count = 0
      for _, param in self.module.named_parameters(recurse=True):
          if (
              param.ds_numel + total_persistent_parameters
              > model_threshold
          ):
              continue
  
          if param.ds_numel < param_threshold:
              params_count += 1
              param.ds_persist = True
              persistent_params.append(param)
              total_persistent_parameters += param.ds_numel
  
      print_rank_0(
          f"Parameter Offload: Total persistent parameters: {total_persistent_parameters} in {params_count} params",
          force=True,
      )
  
      return persistent_params
  
imports: import sys
  import torch
  from torch.cuda import Stream
  from collections import OrderedDict
  from deepspeed.runtime.utils import see_memory_usage
  from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
  from deepspeed.runtime.zero.partition_parameters import _init_external_params
  from deepspeed.runtime.zero.partition_parameters import *
  from deepspeed.runtime.zero.partitioned_param_coordinator import PartitionedParameterCoordinator, iter_params
  from deepspeed import comm
label_desc: Use image node_id 8 for calling the DeepSpeedZeRoOffload obj's underlying member method code with example usage: obj.mark_persistent_parameters(param_threshold, model_threshold) and returns: persistent_params