code: def forward(
      self,
      x,
      time_embed,
      context=None,
      context_mask=None,
      image_mask=None,
  ):
      height, width = x.shape[-2:]
      out = self.in_norm(x, time_embed)
      out = out.reshape(x.shape[0], -1, height * width).permute(0, 2, 1)
      context = context if context is not None else out
      if context_mask is not None:
          context_mask = context_mask.to(dtype=context.dtype)
  
      out = self.attention(out, context, context_mask)
      out = (
          out.permute(0, 2, 1)
          .unsqueeze(-1)
          .reshape(out.shape[0], -1, height, width)
      )
      x = x + out
  
      out = self.out_norm(x, time_embed)
      out = self.feed_forward(out)
      x = x + out
      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 Kandinsky3AttentionBlock obj's underlying member method code with example usage: obj.forward(x, time_embed, context, context_mask, image_mask) and returns: x