code: def test_model_with_attention_head_dim_tuple(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()
  
      with torch.no_grad():
          output = model(**inputs_dict)
  
          if isinstance(output, dict):
              output = output.sample
  
      self.assertIsNotNone(output)
      expected_shape = inputs_dict["sample"].shape
      self.assertEqual(
          output.shape,
          expected_shape,
          "Input and output shapes do not match",
      )
  
imports: import copy
  import gc
  import os
  import tempfile
  import unittest
  from collections import OrderedDict
  import torch
  from parameterized import parameterized
  from pytest import mark
  from diffusers import UNet2DConditionModel
  from diffusers.models.attention_processor import CustomDiffusionAttnProcessor, IPAdapterAttnProcessor
  from diffusers.models.embeddings import ImageProjection, IPAdapterPlusImageProjection
  from diffusers.utils import logging
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import backend_empty_cache, enable_full_determinism, floats_tensor, load_hf_numpy, require_torch_accelerator, require_torch_accelerator_with_fp16, require_torch_accelerator_with_training, require_torch_gpu, skip_mps, slow, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
label_desc: Use image node_id 7 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.test_model_with_attention_head_dim_tuple() without return types