code: def __init__(self) -> None:
      super().__init__()
      self.embed_image = ImageEmbedding()
      self.embed_time = TimestepEmbedding_()
  
      down_0 = nn.ModuleList(
          [
              ConvResblock(320, 320),
              ConvResblock(320, 320),
              ConvResblock(320, 320),
              Downsample(320),
          ]
      )
      down_1 = nn.ModuleList(
          [
              ConvResblock(320, 640),
              ConvResblock(640, 640),
              ConvResblock(640, 640),
              Downsample(640),
          ]
      )
      down_2 = nn.ModuleList(
          [
              ConvResblock(640, 1024),
              ConvResblock(1024, 1024),
              ConvResblock(1024, 1024),
              Downsample(1024),
          ]
      )
      down_3 = nn.ModuleList(
          [
              ConvResblock(1024, 1024),
              ConvResblock(1024, 1024),
              ConvResblock(1024, 1024),
          ]
      )
      self.down = nn.ModuleList(
          [
              down_0,
              down_1,
              down_2,
              down_3,
          ]
      )
  
      self.mid = nn.ModuleList(
          [
              ConvResblock(1024, 1024),
              ConvResblock(1024, 1024),
          ]
      )
  
      up_3 = nn.ModuleList(
          [
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 * 2, 1024),
              Upsample(1024),
          ]
      )
      up_2 = nn.ModuleList(
          [
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 * 2, 1024),
              ConvResblock(1024 + 640, 1024),
              Upsample(1024),
          ]
      )
      up_1 = nn.ModuleList(
          [
              ConvResblock(1024 + 640, 640),
              ConvResblock(640 * 2, 640),
              ConvResblock(640 * 2, 640),
              ConvResblock(320 + 640, 640),
              Upsample(640),
          ]
      )
      up_0 = nn.ModuleList(
          [
              ConvResblock(320 + 640, 320),
              ConvResblock(320 * 2, 320),
              ConvResblock(320 * 2, 320),
              ConvResblock(320 * 2, 320),
          ]
      )
      self.up = nn.ModuleList(
          [
              up_0,
              up_1,
              up_2,
              up_3,
          ]
      )
  
      self.output = ImageUnembedding()
  
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 1 to create a new ConvUNetVAE object from inherited base classes: nn with example: obj = ConvUNetVAE()