code: def __call__(
      self,
      attn: Attention,
      hidden_states,
      encoder_hidden_states=None,
      attention_mask=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)
  
      is_cross = encoder_hidden_states is not None
      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
      )
  
      # only need to store attention maps during the Attend and Excite process
      if attention_probs.requires_grad:
          self.attnstore(attention_probs, is_cross, self.place_in_unet)
  
      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)
  
      return hidden_states
  
imports: import inspect
  import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch.nn import functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.attention_processor import Attention
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion import StableDiffusionPipelineOutput
  from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 2 for calling the AttendExciteAttnProcessor obj's underlying member method code with example usage: obj.__call__(attn, hidden_states, encoder_hidden_states, attention_mask) and returns: hidden_states