code: def forward(self, x, t) -> torch.Tensor:
      x_skip = x
  
      t = self.f_t(F.silu(t))
      t_1, t_2 = t.chunk(2, dim=1)
      t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1
      t_2 = t_2.unsqueeze(2).unsqueeze(3)
  
      gn_1 = F.silu(self.gn_1(x))
      upsample = F.upsample_nearest(gn_1, scale_factor=2)
      f_1 = self.f_1(upsample)
      gn_2 = self.gn_2(f_1)
  
      f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2)))
  
      return f_2 + F.upsample_nearest(x_skip, scale_factor=2)
  
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 Upsample obj's underlying member method code with example usage: obj.forward(x, t) and returns: unknown