code: def transformer_ada_norm_to_diffusers_checkpoint(
      checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix
  ):
      return {
          f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[
              f"{ada_norm_prefix}.emb.weight"
          ],
          f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[
              f"{ada_norm_prefix}.linear.weight"
          ],
          f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[
              f"{ada_norm_prefix}.linear.bias"
          ],
      }
  
imports: import argparse
  import tempfile
  import torch
  import yaml
  from accelerate import init_empty_weights, load_checkpoint_and_dispatch
  from transformers import CLIPTextModel, CLIPTokenizer
  from yaml.loader import FullLoader
  from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
  from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
label_desc: Use image node_id 13 for calling a global function with example usage: transformer_ada_norm_to_diffusers_checkpoint(checkpoint) and returns: dict