code: def round_timesteps(
      timesteps, total_timesteps, n_distilled_steps, truncate_start=True
  ):
      with torch.no_grad():
          space = torch.div(
              total_timesteps, n_distilled_steps, rounding_mode="floor"
          )
          rounded_timesteps = (
              torch.div(timesteps, space, rounding_mode="floor") + 1
          ) * space
          if truncate_start:
              rounded_timesteps[
                  rounded_timesteps == total_timesteps
              ] -= space
          else:
              rounded_timesteps[
                  rounded_timesteps == total_timesteps
              ] -= space
              rounded_timesteps[rounded_timesteps == 0] += space
          return rounded_timesteps
  
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 ConsistencyDecoder obj's underlying member method code with example usage: obj.round_timesteps(timesteps, total_timesteps, n_distilled_steps, truncate_start) and returns: rounded_timesteps