code: def test_set_attention_slice_list(self):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()
  
      # there are 32 sliceable layers
      slice_list = 16 * [2, 3]
      unet = self.get_unet_model()
      unet.set_attention_slice(slice_list)
  
      latents = self.get_latents(33)
      encoder_hidden_states = self.get_encoder_hidden_states(33)
      timestep = 1
  
      with torch.no_grad():
          _ = unet(
              latents,
              timestep=timestep,
              encoder_hidden_states=encoder_hidden_states,
          ).sample
  
      mem_bytes = torch.cuda.max_memory_allocated()
  
      assert mem_bytes < 5 * 10**9
  
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 8 for calling the UNet2DConditionModelIntegrationTests obj's underlying member method code with example usage: obj.test_set_attention_slice_list() without return types