code: def movq(*, args, checkpoint_map_location):
      print("loading movq")
  
      movq_checkpoint = torch.load(
          args.movq_checkpoint_path,
          map_location=checkpoint_map_location,
      )
  
      movq_model = movq_model_from_original_config()
  
      movq_diffusers_checkpoint = (
          movq_original_checkpoint_to_diffusers_checkpoint(
              movq_model, movq_checkpoint
          )
      )
  
      del movq_checkpoint
  
      load_checkpoint_to_model(
          movq_diffusers_checkpoint, movq_model, strict=True
      )
  
      print("done loading movq")
  
      return movq_model
  
imports: import argparse
  import os
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from diffusers import UNet2DConditionModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.models.vq_model import VQModel
label_desc: Use image node_id 32 for calling a global function with example usage: movq() and returns: movq_model