code: def forward(self, x, t, features) -> torch.Tensor:
      converted = hasattr(self, "converted") and self.converted
  
      x = torch.cat(
          [x, F.upsample_nearest(features, scale_factor=8)], dim=1
      )
  
      if converted:
          t = self.time_embedding(self.time_proj(t))
      else:
          t = self.embed_time(t)
  
      x = self.embed_image(x)
  
      skips = [x]
      for i, down in enumerate(self.down):
          if converted and i in [0, 1, 2, 3]:
              x, skips_ = down(x, t)
              for skip in skips_:
                  skips.append(skip)
          else:
              for block in down:
                  x = block(x, t)
                  skips.append(x)
          print(x.float().abs().sum())
  
      if converted:
          x = self.mid(x, t)
      else:
          for i in range(2):
              x = self.mid[i](x, t)
      print(x.float().abs().sum())
  
      for i, up in enumerate(self.up[::-1]):
          if converted and i in [0, 1, 2, 3]:
              skip_4 = skips.pop()
              skip_3 = skips.pop()
              skip_2 = skips.pop()
              skip_1 = skips.pop()
              skips_ = (skip_1, skip_2, skip_3, skip_4)
              x = up(x, skips_, t)
          else:
              for block in up:
                  if isinstance(block, ConvResblock):
                      x = torch.concat([x, skips.pop()], dim=1)
                  x = block(x, t)
  
      return self.output(x)
  
imports: import math
  import os
  import urllib
  import warnings
  from argparse import ArgumentParser
  import torch
  import torch.nn
  import torch.nn.functional
  from huggingface_hub.utils import insecure_hashlib
  from safetensors.torch import load_file
  from tqdm import tqdm
  from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
  from diffusers.models.autoencoders.vae import Encoder
  from diffusers.models.embeddings import TimestepEmbedding
  from diffusers.models.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
label_desc: Use image node_id 2 for calling the ConvUNetVAE obj's underlying member method code with example usage: obj.forward(x, t, features) and returns: self