code: def store_latent(decoded):
      state.current_latent = decoded
  
      if (
          opts.live_previews_enable
          and opts.show_progress_every_n_steps > 0
          and shared.state.sampling_step
          % opts.show_progress_every_n_steps
          == 0
      ):
          if not shared.parallel_processing_allowed:
              shared.state.assign_current_image(
                  sample_to_image(decoded)
              )
  
imports: import inspect
  from collections import namedtuple
  import numpy
  import torch
  from PIL import Image
  from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
  from modules.shared import opts, state
  import k_diffusion.sampling
label_desc: Use image node_id 8 for calling a global function with example usage: store_latent(decoded) without return types