code: def load_checkpoint_to_model(checkpoint, model, strict=False):
      with tempfile.NamedTemporaryFile() as file:
          torch.save(checkpoint, file.name)
          del checkpoint
          if strict:
              model.load_state_dict(torch.load(file.name), strict=True)
          else:
              load_checkpoint_and_dispatch(
                  model, file.name, device_map="auto"
              )
  
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 27 for calling a global function with example usage: load_checkpoint_to_model(checkpoint, model, strict) without return types