code: def __init__(self, in_channels=320) -> None:
      super().__init__()
      self.f_t = nn.Linear(1280, in_channels * 2)
  
      self.gn_1 = nn.GroupNorm(32, in_channels)
      self.f_1 = nn.Conv2d(
          in_channels, in_channels, kernel_size=3, padding=1
      )
      self.gn_2 = nn.GroupNorm(32, in_channels)
  
      self.f_2 = nn.Conv2d(
          in_channels, in_channels, kernel_size=3, padding=1
      )
  
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 Downsample object from inherited base classes: nn with example: obj = Downsample(in_channels)