code: def test_model_attention_slicing(self):
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
  
      init_dict["attention_head_dim"] = (8, 16)
  
      model = self.model_class(**init_dict)
      model.to(torch_device)
      model.eval()
  
      model.set_attention_slice("auto")
      with torch.no_grad():
          output = model(**inputs_dict)
      assert output is not None
  
      model.set_attention_slice("max")
      with torch.no_grad():
          output = model(**inputs_dict)
      assert output is not None
  
      model.set_attention_slice(2)
      with torch.no_grad():
          output = model(**inputs_dict)
      assert output is not None
  
imports: import copy
  import gc
  import os
  import tempfile
  import unittest
  import torch
  from parameterized import parameterized
  from pytest import mark
  from diffusers import UNet2DConditionModel
  from diffusers.models.attention_processor import CustomDiffusionAttnProcessor
  from diffusers.utils import logging
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
label_desc: Use image node_id 12 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.test_model_attention_slicing() without return types