code: def setup_zero_stage3_hooks(self):
      self.hierarchy = 0
  
      # reset step if in inference mode
      @instrument_w_nvtx
      def _end_of_forward_hook(module, *args):
          if not torch._C.is_grad_enabled():
              self.get_param_coordinator(training=False).reset_step()
  
      # likely one of them should be enough but just to be safe
      self._register_hooks_recursively(self.module)
      self.module.register_forward_hook(_end_of_forward_hook)
  
      # Add top module to stack trace
      global FWD_MODULE_STACK
      FWD_MODULE_STACK.append(self.module)
  
imports: import sys
  import torch
  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, InflightParamRegistry, iter_params
  from deepspeed import comm
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 8 for calling the DeepSpeedZeRoOffload obj's underlying member method code with example usage: obj.setup_zero_stage3_hooks() without return types