code: def __init__(self, num_channels, context_dim, head_dim=64):
      super().__init__()
      self.attention = Attention(
          context_dim,
          context_dim,
          dim_head=head_dim,
          out_dim=num_channels,
          out_bias=False,
      )
  
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 1 to create a new Kandinsky3AttentionPooling object from inherited base classes: nn with example: obj = Kandinsky3AttentionPooling(num_channels, context_dim, head_dim)