code: def cpu_offload_with_hook(
      model: torch.nn.Module,
      execution_device: Optional[Union[int, str, torch.device]] = None,
      prev_module_hook: Optional[UserCpuOffloadHook] = None,
  ):
      """
      Offloads a model on the CPU and puts it back to an execution device when executed. The difference with
      [`cpu_offload`] is that the model stays on the execution device after the forward and is only offloaded again when
      the `offload` method of the returned `hook` is called. Useful for pipelines running a model in a loop.
  
      Args:
          model (`torch.nn.Module`):
              The model to offload.
          execution_device(`str`, `int` or `torch.device`, *optional*):
              The device on which the model should be executed. Will default to the MPS device if it's available, then
              GPU 0 if there is a GPU, and finally to the CPU.
          prev_module_hook (`UserCpuOffloadHook`, *optional*):
              The hook sent back by this function for a previous model in the pipeline you are running. If passed, its
              offload method will be called just before the forward of the model to which this hook is attached.
  
      Example:
  
      ```py
      model_1, hook_1 = cpu_offload_with_hook(model_1, cuda_device)
      model_2, hook_2 = cpu_offload_with_hook(model_2, cuda_device, prev_module_hook=hook_1)
      model_3, hook_3 = cpu_offload_with_hook(model_3, cuda_device, prev_module_hook=hook_2)
  
      hid_1 = model_1(input)
      for i in range(50):
          # model1 is offloaded on the CPU at the first iteration, model 2 stays on the GPU for this whole loop.
          hid_2 = model_2(hid_1)
      # model2 is offloaded to the CPU just before this forward.
      hid_3 = model_3(hid_3)
  
      # For model3, you need to manually call the hook offload method.
      hook_3.offload()
      ```
      """
      hook = CpuOffload(
          execution_device=execution_device,
          prev_module_hook=prev_module_hook,
      )
      add_hook_to_module(model, hook, append=True)
      user_hook = UserCpuOffloadHook(model, hook)
      return model, user_hook
  
file_path: accelerate/src/accelerate/big_modeling.py
imports: import logging
  import os
  from contextlib import contextmanager
  from functools import wraps
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn
  from .hooks import AlignDevicesHook, CpuOffload, UserCpuOffloadHook, add_hook_to_module, attach_align_device_hook, attach_align_device_hook_on_blocks
  from .utils import OffloadedWeightsLoader, check_device_map, extract_submodules_state_dict, find_tied_parameters, get_balanced_memory, infer_auto_device_map, is_npu_available, is_torch_version, load_checkpoint_in_model, offload_state_dict, parse_flag_from_env, retie_parameters
label_desc: Use image node_id 4 for calling a global function with example usage: cpu_offload_with_hook(model, execution_device, prev_module_hook) and returns: model,  user_hook