code: def test_align_devices_as_model_parallelism(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
      add_hook_to_module(
          model.linear1, AlignDevicesHook(execution_device=0)
      )
      add_hook_to_module(
          model.batchnorm, AlignDevicesHook(execution_device=0)
      )
      add_hook_to_module(
          model.linear2, AlignDevicesHook(execution_device=1)
      )
  
      self.assertEqual(model.linear1.weight.device, torch.device(0))
      self.assertEqual(model.batchnorm.weight.device, torch.device(0))
      self.assertEqual(
          model.batchnorm.running_mean.device, torch.device(0)
      )
      self.assertEqual(model.linear2.weight.device, torch.device(1))
  
      # We can still make a forward pass. The input does not need to be on any particular device
      x = torch.randn(2, 3)
      output = model(x)
      self.assertEqual(output.device, torch.device(1))
  
      # We can add a general hook to put back output on same device as input.
      add_hook_to_module(model, AlignDevicesHook(io_same_device=True))
      x = torch.randn(2, 3).to(0)
      output = model(x)
      self.assertEqual(output.device, torch.device(0))
  
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 6 for calling the HooksModelTester obj's underlying member method code with example usage: obj.test_align_devices_as_model_parallelism() without return types