code: def test_model_xattn_padding(self):
      (
          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
  
          batch, tokens, _ = cond.shape
          keeplast_mask = (
              (torch.arange(tokens) == tokens - 1)
              .expand(batch, -1)
              .to(cond.device, torch.bool)
          )
          keeplast_out = model(
              **{**inputs_dict, "encoder_attention_mask": keeplast_mask}
          ).sample
          assert not keeplast_out.allclose(
              full_cond_out
          ), "a 'keep last token' mask should change the result"
  
          trunc_mask = torch.zeros(
              batch, tokens - 1, device=cond.device, dtype=torch.bool
          )
          trunc_mask_out = model(
              **{**inputs_dict, "encoder_attention_mask": trunc_mask}
          ).sample
          assert trunc_mask_out.allclose(
              keeplast_out
          ), "a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask."
  
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 17 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.test_model_xattn_padding() without return types