code: def test_accelerate_compatibility_cpu_gpu(
      self, keep_original_model=True, max_memory=None
  ):
      hf_model = AutoModelForCausalLM.from_pretrained(
          "gpt2", device_map="auto", max_memory=max_memory
      ).eval()
      bt_model = BetterTransformer.transform(
          hf_model,
          keep_original_model=keep_original_model,
          max_memory=max_memory,
      )
  
      inputs_ids = torch.LongTensor(
          [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]
      )
      attention_mask = torch.Tensor(
          [[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0]]
      )
  
      # Check that the model has been dispatched on CPU and GPU
      self.assertSetEqual(
          set(hf_model.hf_device_map.values()), set(max_memory)
      )
      self.assertSetEqual(
          set(bt_model.hf_device_map.values()), set(max_memory)
      )
  
      # Check that the model has weights on GPU and CPU
      self.assertEqual(
          bt_model.transformer.h[0].mlp.c_fc.weight.device,
          torch.device("cuda:0"),
      )
  
      # Weights that are offloaded on the CPU are offloaded on the `meta` device
      if "cpu" in set(max_memory):
          self.assertEqual(
              bt_model.transformer.h[-1].mlp.c_fc.weight.device,
              torch.device("meta"),
          )
  
      with torch.inference_mode():
          output_bt = bt_model(
              inputs_ids, attention_mask=attention_mask
          )
          output_hf = hf_model(
              inputs_ids, attention_mask=attention_mask
          )
  
      self.assertEqual(output_bt[0].device, torch.device("cpu"))
      self.assertTrue(
          torch.allclose(output_bt[0], output_hf[0], atol=1e-3)
      )
  
      gc.collect()
  
imports: import gc
  import unittest
  import pytest
  import torch
  from parameterized import parameterized
  from testing_utils import MODELS_DICT, BetterTransformersTestMixin
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils import BloomDummyPastKeyValuesGenerator, DummyPastKeyValuesGenerator, NormalizedConfigManager
  from optimum.utils.testing_utils import grid_parameters, require_accelerate, require_torch_gpu
label_desc: Use image node_id 11 for calling the BetterTransformersDecoderTest obj's underlying member method code with example usage: obj.test_accelerate_compatibility_cpu_gpu(keep_original_model, max_memory) without return types