code: def __call__(self, attn, is_cross: bool, place_in_unet: str):
      if self.cur_att_layer >= 0 and is_cross:
          if attn.shape[1] == np.prod(self.attn_res):
              self.step_store[place_in_unet].append(attn)
  
      self.cur_att_layer += 1
      if self.cur_att_layer == self.num_att_layers:
          self.cur_att_layer = 0
          self.between_steps()
  
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 .None import StableDiffusionPipelineOutput
  from .safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 2 for calling the AttentionStore obj's underlying member method code with example usage: obj.__call__(attn, is_cross, place_in_unet) without return types