code: def onload_layer(layer):
      r"""
      A utility for modifying a module containing one or more tuners and a base layer, any of which are offloaded to the
      CPU or disk. Moves a module's sub-modules to the execution device before some action is performed, after that the
      base layer state dictionary is re-assigned (if that layer was offloaded to the disk) and finally the parameters are
      offloaded.
  
      If the module has no offloaded sub-modules, this function does nothing.
  
      Args:
          layer ('torch.nn.Module'):
              layer with tuners to be merged
      """
  
      offloaded_modules = []
      for name, module in layer.named_modules():
          if name in ["", "base_layer"]:
              continue
          if (
              hasattr(module, "_hf_hook")
              and isinstance(module._hf_hook, AlignDevicesHook)
              and module._hf_hook.offload
          ):
              module._hf_hook.pre_forward(module)
              offloaded_modules.append(module)
  
      base_layer_offload = False
      if hasattr(layer, "base_layer") and (
          hasattr(layer.base_layer, "_hf_hook")
          and isinstance(layer.base_layer._hf_hook, AlignDevicesHook)
          and layer.base_layer._hf_hook.offload
      ):
          if (
              torch.device("meta")
              in layer.base_layer._hf_hook.original_devices.values()
          ):
              # retrieve the name of the original disk-offload directory
              offload_folder = (
                  layer.base_layer._hf_hook.weights_map.dataset.save_folder
              )
          layer.base_layer._hf_hook.pre_forward(layer.base_layer)
          base_layer_offload = True
  
      yield
  
      for module in offloaded_modules:
          module._hf_hook.post_forward(module, torch.tensor([]))
  
      if base_layer_offload:
          # re-make weights map (must be on cpu to send params to the disk via memmap if disk offload)
          layer.base_layer._hf_hook.weights_map = {
              name: param.to("cpu")
              for name, param in named_module_tensors(layer.base_layer)
          }
          # offload weights map to disk if original device is the disk
          if (
              torch.device("meta")
              in layer.base_layer._hf_hook.original_devices.values()
          ):
              # rewrite directory with merged weights
              offload_state_dict(
                  offload_folder, layer.base_layer._hf_hook.weights_map
              )
          layer.base_layer._hf_hook.post_forward(
              layer.base_layer, torch.tensor([])
          )
  
imports: from __future__ import annotations
  import logging
  import re
  import warnings
  from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from typing import Any, List, Optional, Union
  import torch
  from accelerate.hooks import AlignDevicesHook
  from accelerate.utils import named_module_tensors, offload_state_dict
  from torch import nn
  from transformers import PreTrainedModel
  from transformers.pytorch_utils import Conv1D
  from peft.utils import INCLUDE_LINEAR_LAYERS_SHORTHAND
  from ..config import PeftConfig
  from ..utils import ModulesToSaveWrapper, _get_submodules
label_desc: Use image node_id 1 for calling a global function with example usage: onload_layer(layer) without return types