code: def test_align_devices_as_cpu_offload(self):
      model = ModelForTest()
  
      # Everything is on CPU
      self.assertEqual(model.linear1.weight.device, torch.device("cpu"))
      self.assertEqual(
          model.batchnorm.weight.device, torch.device("cpu")
      )
      self.assertEqual(model.linear2.weight.device, torch.device("cpu"))
  
      # This will move each submodule on different devices
      hook_kwargs = {
          "execution_device": 0 if torch.cuda.is_available() else "cpu",
          "offload": True,
      }
  
      add_hook_to_module(model.linear1, AlignDevicesHook(**hook_kwargs))
      add_hook_to_module(
          model.batchnorm, AlignDevicesHook(**hook_kwargs)
      )
      add_hook_to_module(model.linear2, AlignDevicesHook(**hook_kwargs))
  
      # Parameters have been offloaded, so on the meta device
      self.assertEqual(
          model.linear1.weight.device, torch.device("meta")
      )
      self.assertEqual(
          model.batchnorm.weight.device, torch.device("meta")
      )
      self.assertEqual(
          model.linear2.weight.device, torch.device("meta")
      )
      # Buffers are not included in the offload by default, so are on the execution device
      device = torch.device(hook_kwargs["execution_device"])
      self.assertEqual(model.batchnorm.running_mean.device, device)
  
      x = torch.randn(2, 3)
      output = model(x)
      self.assertEqual(output.device, device)
  
      # Removing hooks loads back the weights in the model.
      remove_hook_from_module(model.linear1)
      remove_hook_from_module(model.batchnorm)
      remove_hook_from_module(model.linear2)
      self.assertEqual(model.linear1.weight.device, torch.device("cpu"))
      self.assertEqual(
          model.batchnorm.weight.device, torch.device("cpu")
      )
      self.assertEqual(model.linear2.weight.device, torch.device("cpu"))
  
      # Now test with buffers included in the offload
      hook_kwargs = {
          "execution_device": 0 if torch.cuda.is_available() else "cpu",
          "offload": True,
          "offload_buffers": True,
      }
  
      add_hook_to_module(model.linear1, AlignDevicesHook(**hook_kwargs))
      add_hook_to_module(
          model.batchnorm, AlignDevicesHook(**hook_kwargs)
      )
      add_hook_to_module(model.linear2, AlignDevicesHook(**hook_kwargs))
  
      # Parameters have been offloaded, so on the meta device, buffers included
      self.assertEqual(
          model.linear1.weight.device, torch.device("meta")
      )
      self.assertEqual(
          model.batchnorm.weight.device, torch.device("meta")
      )
      self.assertEqual(
          model.linear2.weight.device, torch.device("meta")
      )
      self.assertEqual(
          model.batchnorm.running_mean.device, torch.device("meta")
      )
  
      x = torch.randn(2, 3)
      output = model(x)
      self.assertEqual(output.device, device)
  
      # Removing hooks loads back the weights in the model.
      remove_hook_from_module(model.linear1)
      remove_hook_from_module(model.batchnorm)
      remove_hook_from_module(model.linear2)
      self.assertEqual(model.linear1.weight.device, torch.device("cpu"))
      self.assertEqual(
          model.batchnorm.weight.device, torch.device("cpu")
      )
      self.assertEqual(model.linear2.weight.device, torch.device("cpu"))
  
imports: import inspect
  import unittest
  import torch
  import torch.nn
  from accelerate.hooks import AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules
  from accelerate.test_utils import require_multi_gpu
label_desc: Use image node_id 7 for calling the HooksModelTester obj's underlying member method code with example usage: obj.test_align_devices_as_cpu_offload() without return types