code: def pre_sub_module_forward_function(self, sub_module):
      see_memory_usage(
          f"Before sub module function {sub_module.__class__.__name__}",
          force=False,
      )
      prev_grad_state = (
          torch.is_grad_enabled()
      )  # we don't want to enable grad for sub modules fetching, yet the subfunction need to know if grad is enabled
      torch.set_grad_enabled(False)
      global FWD_MODULE_STACK
      FWD_MODULE_STACK.append(sub_module)
  
      param_coordinator = self.get_param_coordinator(
          training=sub_module.training
      )
      param_coordinator.trace_prologue(sub_module)
      if param_coordinator.is_record_trace():
          param_coordinator.record_module(sub_module)
      param_coordinator.fetch_sub_module(
          sub_module, forward=prev_grad_state
      )
      torch.set_grad_enabled(prev_grad_state)
      see_memory_usage(
          f"Before sub module function {sub_module.__class__.__name__} after fetch",
          force=False,
      )
  
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 11 for calling the DeepSpeedZeRoOffload obj's underlying member method code with example usage: obj.pre_sub_module_forward_function(sub_module) without return types