code: def unet_conv_norm_out(checkpoint, original_unet_prefix):
      diffusers_checkpoint = {}
  
      diffusers_checkpoint.update(
          {
              "conv_norm_out.weight": checkpoint[
                  f"{original_unet_prefix}.out.0.weight"
              ],
              "conv_norm_out.bias": checkpoint[
                  f"{original_unet_prefix}.out.0.bias"
              ],
          }
      )
  
      return diffusers_checkpoint
  
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 15 for calling a global function with example usage: unet_conv_norm_out(checkpoint, original_unet_prefix) and returns: diffusers_checkpoint