code: def test_model_xattn_mask(self, mask_dtype):
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
  
      model = self.model_class(
          **{**init_dict, "attention_head_dim": (8, 16)}
      )
      model.to(torch_device)
      model.eval()
  
      cond = inputs_dict["encoder_hidden_states"]
      with torch.no_grad():
          full_cond_out = model(**inputs_dict).sample
          assert full_cond_out is not None
  
          keepall_mask = torch.ones(
              *cond.shape[:-1], device=cond.device, dtype=mask_dtype
          )
          full_cond_keepallmask_out = model(
              **{**inputs_dict, "encoder_attention_mask": keepall_mask}
          ).sample
          assert full_cond_keepallmask_out.allclose(
              full_cond_out, rtol=1e-05, atol=1e-05
          ), "a 'keep all' mask should give the same result as no mask"
  
          trunc_cond = cond[:, :-1, :]
          trunc_cond_out = model(
              **{**inputs_dict, "encoder_hidden_states": trunc_cond}
          ).sample
          assert not trunc_cond_out.allclose(
              full_cond_out, rtol=1e-05, atol=1e-05
          ), "discarding the last token from our cond should change the result"
  
          batch, tokens, _ = cond.shape
          mask_last = (
              (torch.arange(tokens) < tokens - 1)
              .expand(batch, -1)
              .to(cond.device, mask_dtype)
          )
          masked_cond_out = model(
              **{**inputs_dict, "encoder_attention_mask": mask_last}
          ).sample
          assert masked_cond_out.allclose(
              trunc_cond_out, rtol=1e-05, atol=1e-05
          ), "masking the last token from our cond should be equivalent to truncating that token out of the condition"
  
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 16 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.test_model_xattn_mask(mask_dtype) without return types