code: def get_param_coordinator(self, training):
      if not training in self.param_coordinators:
          self.param_coordinators[
              training
          ] = PartitionedParameterCoordinator(
              prefetch_bucket_sz=self._prefetch_bucket_sz,
              max_reuse_distance_in_numel=self._max_reuse_distance_in_numel,
              max_available_parameters_in_numel=self._max_available_parameters_in_numel,
              allgather_stream=self.__allgather_stream,
              prefetch_nvme=self.offload_device
              == OffloadDeviceEnum.nvme,
          )
  
      return self.param_coordinators[training]
  
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 3 for calling the DeepSpeedZeRoOffload obj's underlying member method code with example usage: obj.get_param_coordinator(training) and returns: self