code: def super_res_unet(*, args, checkpoint_map_location):
      print("loading super resolution unet")
  
      super_res_checkpoint = torch.load(
          args.super_res_unet_checkpoint_path,
          map_location=checkpoint_map_location,
      )
      super_res_checkpoint = super_res_checkpoint["state_dict"]
  
      # model_first_steps
  
      super_res_first_model = (
          super_res_unet_first_steps_model_from_original_config()
      )
  
      super_res_first_steps_checkpoint = super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(
          super_res_first_model, super_res_checkpoint
      )
  
      # model_last_step
      super_res_last_model = (
          super_res_unet_last_step_model_from_original_config()
      )
  
      super_res_last_step_checkpoint = super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(
          super_res_last_model, super_res_checkpoint
      )
  
      del super_res_checkpoint
  
      load_checkpoint_to_model(
          super_res_first_steps_checkpoint,
          super_res_first_model,
          strict=True,
      )
  
      load_checkpoint_to_model(
          super_res_last_step_checkpoint,
          super_res_last_model,
          strict=True,
      )
  
      print("done loading super resolution unet")
  
      return super_res_first_model, super_res_last_model
  
imports: import argparse
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
label_desc: Use image node_id 26 for calling a global function with example usage: super_res_unet() and returns: super_res_first_model,  super_res_last_model