code: def __init__(
      self,
      in_channels,
      out_channels,
      time_embed_dim,
      norm_groups=32,
      compression_ratio=2,
      up_resolutions=4 * [None],
  ):
      super().__init__()
      kernel_sizes = [1, 3, 3, 1]
      hidden_channel = (
          max(in_channels, out_channels) // compression_ratio
      )
      hidden_channels = (
          [(in_channels, hidden_channel)]
          + [(hidden_channel, hidden_channel)] * 2
          + [(hidden_channel, out_channels)]
      )
      self.resnet_blocks = nn.ModuleList(
          [
              Kandinsky3Block(
                  in_channel,
                  out_channel,
                  time_embed_dim,
                  kernel_size,
                  norm_groups,
                  up_resolution,
              )
              for (
                  in_channel,
                  out_channel,
              ), kernel_size, up_resolution in zip(
                  hidden_channels, kernel_sizes, up_resolutions
              )
          ]
      )
      self.shortcut_up_sample = (
          nn.ConvTranspose2d(
              in_channels, in_channels, kernel_size=2, stride=2
          )
          if True in up_resolutions
          else nn.Identity()
      )
      self.shortcut_projection = (
          nn.Conv2d(in_channels, out_channels, kernel_size=1)
          if in_channels != out_channels
          else nn.Identity()
      )
      self.shortcut_down_sample = (
          nn.Conv2d(out_channels, out_channels, kernel_size=2, stride=2)
          if False in up_resolutions
          else 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 Kandinsky3ResNetBlock object from inherited base classes: nn with example: obj = Kandinsky3ResNetBlock(in_channels, out_channels, time_embed_dim, norm_groups, compression_ratio, up_resolutions)