code: def __init__(
      self,
      in_channels: int = 4,
      time_embedding_dim: int = 1536,
      groups: int = 32,
      attention_head_dim: int = 64,
      layers_per_block: Union[int, Tuple[int]] = 3,
      block_out_channels: Tuple[int] = (384, 768, 1536, 3072),
      cross_attention_dim: Union[int, Tuple[int]] = 4096,
      encoder_hid_dim: int = 4096,
  ):
      super().__init__()
  
      # TOOD(Yiyi): Give better name and put into config for the following 4 parameters
      expansion_ratio = 4
      compression_ratio = 2
      add_cross_attention = (False, True, True, True)
      add_self_attention = (False, True, True, True)
  
      out_channels = in_channels
      init_channels = block_out_channels[0] // 2
      self.time_proj = Timesteps(
          init_channels, flip_sin_to_cos=False, downscale_freq_shift=1
      )
  
      self.time_embedding = TimestepEmbedding(
          init_channels,
          time_embedding_dim,
      )
  
      self.add_time_condition = Kandinsky3AttentionPooling(
          time_embedding_dim, cross_attention_dim, attention_head_dim
      )
  
      self.conv_in = nn.Conv2d(
          in_channels, init_channels, kernel_size=3, padding=1
      )
  
      self.encoder_hid_proj = Kandinsky3EncoderProj(
          encoder_hid_dim, cross_attention_dim
      )
  
      hidden_dims = [init_channels] + list(block_out_channels)
      in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))
      text_dims = [
          cross_attention_dim if is_exist else None
          for is_exist in add_cross_attention
      ]
      num_blocks = len(block_out_channels) * [layers_per_block]
      layer_params = [num_blocks, text_dims, add_self_attention]
      rev_layer_params = map(reversed, layer_params)
  
      cat_dims = []
      self.num_levels = len(in_out_dims)
      self.down_blocks = nn.ModuleList([])
      for level, (
          (in_dim, out_dim),
          res_block_num,
          text_dim,
          self_attention,
      ) in enumerate(zip(in_out_dims, *layer_params)):
          down_sample = level != (self.num_levels - 1)
          cat_dims.append(
              out_dim if level != (self.num_levels - 1) else 0
          )
          self.down_blocks.append(
              Kandinsky3DownSampleBlock(
                  in_dim,
                  out_dim,
                  time_embedding_dim,
                  text_dim,
                  res_block_num,
                  groups,
                  attention_head_dim,
                  expansion_ratio,
                  compression_ratio,
                  down_sample,
                  self_attention,
              )
          )
  
      self.up_blocks = nn.ModuleList([])
      for level, (
          (out_dim, in_dim),
          res_block_num,
          text_dim,
          self_attention,
      ) in enumerate(zip(reversed(in_out_dims), *rev_layer_params)):
          up_sample = level != 0
          self.up_blocks.append(
              Kandinsky3UpSampleBlock(
                  in_dim,
                  cat_dims.pop(),
                  out_dim,
                  time_embedding_dim,
                  text_dim,
                  res_block_num,
                  groups,
                  attention_head_dim,
                  expansion_ratio,
                  compression_ratio,
                  up_sample,
                  self_attention,
              )
          )
  
      self.conv_norm_out = nn.GroupNorm(groups, init_channels)
      self.conv_act_out = nn.SiLU()
      self.conv_out = nn.Conv2d(
          init_channels, out_channels, kernel_size=3, padding=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 1 to create a new Kandinsky3UNet object from inherited base classes: ModelMixin, ConfigMixin with example: obj = Kandinsky3UNet(in_channels, time_embedding_dim, groups, attention_head_dim, layers_per_block, block_out_channels, cross_attention_dim, encoder_hid_dim)