code: def forward(
      self,
      sample,
      timestep,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      return_dict=True,
  ):
      if encoder_attention_mask is not None:
          encoder_attention_mask = (
              1 - encoder_attention_mask.to(sample.dtype)
          ) * -10000.0
          encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
  
      if not torch.is_tensor(timestep):
          dtype = (
              torch.float32
              if isinstance(timestep, float)
              else torch.int32
          )
          timestep = torch.tensor(
              [timestep], dtype=dtype, device=sample.device
          )
      elif len(timestep.shape) == 0:
          timestep = timestep[None].to(sample.device)
  
      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      timestep = timestep.expand(sample.shape[0])
      time_embed_input = self.time_proj(timestep).to(sample.dtype)
      time_embed = self.time_embedding(time_embed_input)
  
      encoder_hidden_states = self.encoder_hid_proj(
          encoder_hidden_states
      )
  
      if encoder_hidden_states is not None:
          time_embed = self.add_time_condition(
              time_embed, encoder_hidden_states, encoder_attention_mask
          )
  
      hidden_states = []
      sample = self.conv_in(sample)
      for level, down_sample in enumerate(self.down_blocks):
          sample = down_sample(
              sample,
              time_embed,
              encoder_hidden_states,
              encoder_attention_mask,
          )
          if level != self.num_levels - 1:
              hidden_states.append(sample)
  
      for level, up_sample in enumerate(self.up_blocks):
          if level != 0:
              sample = torch.cat([sample, hidden_states.pop()], dim=1)
          sample = up_sample(
              sample,
              time_embed,
              encoder_hidden_states,
              encoder_attention_mask,
          )
  
      sample = self.conv_norm_out(sample)
      sample = self.conv_act_out(sample)
      sample = self.conv_out(sample)
  
      if not return_dict:
          return (sample,)
      return Kandinsky3UNetOutput(sample=sample)
  
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 6 for calling the Kandinsky3UNet obj's underlying member method code with example usage: obj.forward(sample, timestep, encoder_hidden_states, encoder_attention_mask, return_dict) and returns: Kandinsky3UNetOutput, sample