code: def train_hypernetwork(*args):
      shared.loaded_hypernetworks = []
  
      assert (
          not shared.cmd_opts.lowvram
      ), "Training models with lowvram is not possible"
  
      try:
          sd_hijack.undo_optimizations()
  
          (
              hypernetwork,
              filename,
          ) = modules.hypernetworks.hypernetwork.train_hypernetwork(
              *args
          )
  
          res = f"""
  Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
  Hypernetwork saved to {html.escape(filename)}
  """
          return res, ""
      except Exception:
          raise
      finally:
          shared.sd_model.cond_stage_model.to(devices.device)
          shared.sd_model.first_stage_model.to(devices.device)
          sd_hijack.apply_optimizations()
  
imports: import html
  import gradio
  import modules.hypernetworks.hypernetwork
  from modules import devices, sd_hijack, shared
label_desc: Use image node_id 2 for calling a global function with example usage: train_hypernetwork() and returns: res,  str