code: def prior(*, args, checkpoint_map_location):
      print("loading prior")
  
      prior_checkpoint = torch.load(
          args.prior_checkpoint_path,
          map_location=checkpoint_map_location,
      )
      prior_checkpoint = prior_checkpoint["state_dict"]
  
      clip_stats_checkpoint = torch.load(
          args.clip_stat_path, map_location=checkpoint_map_location
      )
  
      prior_model = prior_model_from_original_config()
  
      prior_diffusers_checkpoint = (
          prior_original_checkpoint_to_diffusers_checkpoint(
              prior_model, prior_checkpoint, clip_stats_checkpoint
          )
      )
  
      del prior_checkpoint
      del clip_stats_checkpoint
  
      load_checkpoint_to_model(
          prior_diffusers_checkpoint, prior_model, strict=True
      )
  
      print("done loading prior")
  
      return prior_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 24 for calling a global function with example usage: prior() and returns: prior_model