code: def __init__(
      self,
      in_channels,
      out_channels,
      time_embed_dim,
      kernel_size=3,
      norm_groups=32,
      up_resolution=None,
  ):
      super().__init__()
      self.group_norm = Kandinsky3ConditionalGroupNorm(
          norm_groups, in_channels, time_embed_dim
      )
      self.activation = nn.SiLU()
      if up_resolution is not None and up_resolution:
          self.up_sample = nn.ConvTranspose2d(
              in_channels, in_channels, kernel_size=2, stride=2
          )
      else:
          self.up_sample = nn.Identity()
  
      padding = int(kernel_size > 1)
      self.projection = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          padding=padding,
      )
  
      if up_resolution is not None and not up_resolution:
          self.down_sample = nn.Conv2d(
              out_channels, out_channels, kernel_size=2, stride=2
          )
      else:
          self.down_sample = nn.Identity()
  
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 Kandinsky3Block object from inherited base classes: nn with example: obj = Kandinsky3Block(in_channels, out_channels, time_embed_dim, kernel_size, norm_groups, up_resolution)