code: def unet_add_embedding(checkpoint):
      diffusers_checkpoint = {}
  
      diffusers_checkpoint.update(
          {
              "add_embedding.text_norm.weight": checkpoint[
                  "ln_model_n.weight"
              ],
              "add_embedding.text_norm.bias": checkpoint[
                  "ln_model_n.bias"
              ],
              "add_embedding.text_proj.weight": checkpoint[
                  "proj_n.weight"
              ],
              "add_embedding.text_proj.bias": checkpoint["proj_n.bias"],
              "add_embedding.image_proj.weight": checkpoint[
                  "img_layer.weight"
              ],
              "add_embedding.image_proj.bias": checkpoint[
                  "img_layer.bias"
              ],
          }
      )
  
      return diffusers_checkpoint
  
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 11 for calling a global function with example usage: unet_add_embedding(checkpoint) and returns: diffusers_checkpoint