code: def load_checkpoint_to_model(checkpoint, model):
      with tempfile.NamedTemporaryFile(delete=False) as file:
          torch.save(checkpoint, file.name)
          del checkpoint
          model.load_state_dict(torch.load(file.name), strict=False)
  
      os.remove(file.name)
  
imports: import argparse
  import os
  import tempfile
  import torch
  from lavis.models import load_model_and_preprocess
  from transformers import CLIPTokenizer
  from transformers.models.blip_2.configuration_blip_2 import Blip2Config
  from diffusers import AutoencoderKL, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines import BlipDiffusionPipeline
  from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
  from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
  from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
label_desc: Use image node_id 11 for calling a global function with example usage: load_checkpoint_to_model(checkpoint, model) without return types