code: def __init__(
      self,
      num_channels,
      time_embed_dim,
      context_dim=None,
      norm_groups=32,
      head_dim=64,
      expansion_ratio=4,
  ):
      super().__init__()
      self.in_norm = Kandinsky3ConditionalGroupNorm(
          norm_groups, num_channels, time_embed_dim
      )
      self.attention = Attention(
          num_channels,
          context_dim or num_channels,
          dim_head=head_dim,
          out_dim=num_channels,
          out_bias=False,
      )
  
      hidden_channels = expansion_ratio * num_channels
      self.out_norm = Kandinsky3ConditionalGroupNorm(
          norm_groups, num_channels, time_embed_dim
      )
      self.feed_forward = nn.Sequential(
          nn.Conv2d(
              num_channels, hidden_channels, kernel_size=1, bias=False
          ),
          nn.SiLU(),
          nn.Conv2d(
              hidden_channels, num_channels, kernel_size=1, 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 Kandinsky3AttentionBlock object from inherited base classes: nn with example: obj = Kandinsky3AttentionBlock(num_channels, time_embed_dim, context_dim, norm_groups, head_dim, expansion_ratio)