code: def resnet_to_diffusers_checkpoint(
      checkpoint, *, diffusers_resnet_prefix, resnet_prefix
  ):
      diffusers_checkpoint = {
          f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[
              f"{resnet_prefix}.in_layers.0.weight"
          ],
          f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[
              f"{resnet_prefix}.in_layers.0.bias"
          ],
          f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[
              f"{resnet_prefix}.in_layers.2.weight"
          ],
          f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[
              f"{resnet_prefix}.in_layers.2.bias"
          ],
          f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[
              f"{resnet_prefix}.emb_layers.1.weight"
          ],
          f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[
              f"{resnet_prefix}.emb_layers.1.bias"
          ],
          f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[
              f"{resnet_prefix}.out_layers.0.weight"
          ],
          f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[
              f"{resnet_prefix}.out_layers.0.bias"
          ],
          f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[
              f"{resnet_prefix}.out_layers.3.weight"
          ],
          f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[
              f"{resnet_prefix}.out_layers.3.bias"
          ],
      }
  
      skip_connection_prefix = f"{resnet_prefix}.skip_connection"
  
      if f"{skip_connection_prefix}.weight" in checkpoint:
          diffusers_checkpoint.update(
              {
                  f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[
                      f"{skip_connection_prefix}.weight"
                  ],
                  f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[
                      f"{skip_connection_prefix}.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 20 for calling a global function with example usage: resnet_to_diffusers_checkpoint(checkpoint) and returns: diffusers_checkpoint