code: def forward(
      self,
      x,
      time_embed,
      context=None,
      context_mask=None,
      image_mask=None,
  ):
      if self.self_attention:
          x = self.attentions[0](x, time_embed, image_mask=image_mask)
  
      for attention, resnet_in, resnet_out in zip(
          self.attentions[1:], self.resnets_in, self.resnets_out
      ):
          x = resnet_in(x, time_embed)
          if self.context_dim is not None:
              x = attention(
                  x, time_embed, context, context_mask, image_mask
              )
          x = resnet_out(x, time_embed)
      return x
  
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 2 for calling the Kandinsky3DownSampleBlock obj's underlying member method code with example usage: obj.forward(x, time_embed, context, context_mask, image_mask) and returns: x