code: def test_attach_align_device_hook_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
      execution_device = 0 if torch.cuda.is_available() else "cpu"
      attach_align_device_hook(
          model, execution_device=execution_device, offload=True
      )
  
      # 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(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_submodules(model)
      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
      attach_align_device_hook(
          model,
          execution_device=execution_device,
          offload=True,
          offload_buffers=True,
      )
  
      # 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_submodules(model)
      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 8 for calling the HooksModelTester obj's underlying member method code with example usage: obj.test_attach_align_device_hook_as_cpu_offload() without return types