code: def test_special_attn_proc(self):
      class AttnEasyProc(torch.nn.Module):
          def __init__(self, num):
              super().__init__()
              self.weight = torch.nn.Parameter(torch.tensor(num))
              self.is_run = False
              self.number = 0
              self.counter = 0
  
          def __call__(
              self,
              attn,
              hidden_states,
              encoder_hidden_states=None,
              attention_mask=None,
              number=None,
          ):
              batch_size, sequence_length, _ = hidden_states.shape
              attention_mask = attn.prepare_attention_mask(
                  attention_mask, sequence_length, batch_size
              )
  
              query = attn.to_q(hidden_states)
  
              encoder_hidden_states = (
                  encoder_hidden_states
                  if encoder_hidden_states is not None
                  else hidden_states
              )
              key = attn.to_k(encoder_hidden_states)
              value = attn.to_v(encoder_hidden_states)
  
              query = attn.head_to_batch_dim(query)
              key = attn.head_to_batch_dim(key)
              value = attn.head_to_batch_dim(value)
  
              attention_probs = attn.get_attention_scores(
                  query, key, attention_mask
              )
              hidden_states = torch.bmm(attention_probs, value)
              hidden_states = attn.batch_to_head_dim(hidden_states)
  
              # linear proj
              hidden_states = attn.to_out[0](hidden_states)
              # dropout
              hidden_states = attn.to_out[1](hidden_states)
  
              hidden_states += self.weight
  
              self.is_run = True
              self.counter += 1
              self.number = number
  
              return hidden_states
  
      # enable deterministic behavior for gradient checkpointing
      (
          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)
  
      processor = AttnEasyProc(5.0)
  
      model.set_attn_processor(processor)
      model(
          **inputs_dict, cross_attention_kwargs={"number": 123}
      ).sample
  
      assert processor.counter == 12
      assert processor.is_run
      assert processor.number == 123
  
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 15 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.test_special_attn_proc() and returns: hidden_states