code: def forward(self, x, context, context_mask=None):
      context_mask = context_mask.to(dtype=context.dtype)
      context = self.attention(
          context.mean(dim=1, keepdim=True), context, context_mask
      )
      return x + context.squeeze(1)
  
imports: from dataclasses import dataclass
  from typing import Dict, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging
  from .attention_processor import Attention, AttentionProcessor, AttnProcessor
  from .embeddings import TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
label_desc: Use image node_id 2 for calling the Kandinsky3AttentionPooling obj's underlying member method code with example usage: obj.forward(x, context, context_mask) and returns: unknown