code: def check_set_module_tensor_for_device(self, model, device1, device2):
      self.assertEqual(
          model.linear1.weight.device, torch.device(device1)
      )
  
      with self.subTest(
          "Access by submodule and direct name for a parameter"
      ):
          set_module_tensor_to_device(model.linear1, "weight", device2)
          self.assertEqual(
              model.linear1.weight.device, torch.device(device2)
          )
  
          if torch.device(device2) == torch.device("meta"):
              with self.assertRaises(ValueError):
                  # We need a `value` to set the weight back on device1
                  set_module_tensor_to_device(
                      model.linear1, "weight", device1
                  )
  
              set_module_tensor_to_device(
                  model.linear1,
                  "weight",
                  device1,
                  value=torch.randn(4, 3),
              )
          else:
              set_module_tensor_to_device(
                  model.linear1, "weight", device1
              )
          self.assertEqual(
              model.linear1.weight.device, torch.device(device1)
          )
  
      with self.subTest(
          "Access by module and full name for a parameter"
      ):
          set_module_tensor_to_device(model, "linear1.weight", device2)
          self.assertEqual(
              model.linear1.weight.device, torch.device(device2)
          )
  
          if torch.device(device2) == torch.device("meta"):
              with self.assertRaises(ValueError):
                  # We need a `value` to set the weight back on device1
                  set_module_tensor_to_device(
                      model, "linear1.weight", device1
                  )
              set_module_tensor_to_device(
                  model,
                  "linear1.weight",
                  device1,
                  value=torch.randn(4, 3),
              )
          else:
              set_module_tensor_to_device(
                  model, "linear1.weight", device1
              )
          self.assertEqual(
              model.linear1.weight.device, torch.device(device1)
          )
  
      self.assertEqual(
          model.batchnorm.running_mean.device, torch.device(device1)
      )
  
      with self.subTest(
          "Access by submodule and direct name for a buffer"
      ):
          set_module_tensor_to_device(
              model.batchnorm, "running_mean", device2
          )
          self.assertEqual(
              model.batchnorm.running_mean.device, torch.device(device2)
          )
  
          if torch.device(device2) == torch.device("meta"):
              with self.assertRaises(ValueError):
                  # We need a `value` to set the weight back on device1
                  set_module_tensor_to_device(
                      model.batchnorm, "running_mean", device1
                  )
              set_module_tensor_to_device(
                  model.batchnorm,
                  "running_mean",
                  device1,
                  value=torch.randn(4),
              )
          else:
              set_module_tensor_to_device(
                  model.batchnorm, "running_mean", device1
              )
          self.assertEqual(
              model.batchnorm.running_mean.device, torch.device(device1)
          )
  
      with self.subTest(
          "Access by module and full name for a parameter"
      ):
          set_module_tensor_to_device(
              model, "batchnorm.running_mean", device2
          )
          self.assertEqual(
              model.batchnorm.running_mean.device, torch.device(device2)
          )
  
          if torch.device(device2) == torch.device("meta"):
              with self.assertRaises(ValueError):
                  # We need a `value` to set the weight back on CPU
                  set_module_tensor_to_device(
                      model, "batchnorm.running_mean", device1
                  )
  
              set_module_tensor_to_device(
                  model,
                  "batchnorm.running_mean",
                  device1,
                  value=torch.randn(4),
              )
          else:
              set_module_tensor_to_device(
                  model, "batchnorm.running_mean", device1
              )
          self.assertEqual(
              model.batchnorm.running_mean.device, torch.device(device1)
          )
  
imports: import json
  import os
  import tempfile
  import unittest
  from collections import OrderedDict
  import torch
  import torch.nn
  from safetensors.torch import save_file
  from accelerate import init_empty_weights
  from accelerate.test_utils import require_cuda, require_huggingface_suite, require_multi_gpu
  from accelerate.utils.modeling import check_device_map, clean_device_map, compute_module_sizes, convert_file_size_to_int, find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, load_state_dict, named_module_tensors, retie_parameters, set_module_tensor_to_device
label_desc: Use image node_id 1 for calling the ModelingUtilsTester obj's underlying member method code with example usage: obj.check_set_module_tensor_for_device(model, device1, device2) without return types