code: def __init__(self, n_time=1024, n_emb=320, n_out=1280) -> None:
      super().__init__()
      self.emb = nn.Embedding(n_time, n_emb)
      self.f_1 = nn.Linear(n_emb, n_out)
      self.f_2 = nn.Linear(n_out, n_out)
  
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 TimestepEmbedding_ object from inherited base classes: nn with example: obj = TimestepEmbedding_(n_time, n_emb, n_out)