code: def transformer_attention_to_diffusers_checkpoint(
      checkpoint, *, diffusers_attention_prefix, attention_prefix
  ):
      return {
          # key
          f"{diffusers_attention_prefix}.to_k.weight": checkpoint[
              f"{attention_prefix}.key.weight"
          ],
          f"{diffusers_attention_prefix}.to_k.bias": checkpoint[
              f"{attention_prefix}.key.bias"
          ],
          # query
          f"{diffusers_attention_prefix}.to_q.weight": checkpoint[
              f"{attention_prefix}.query.weight"
          ],
          f"{diffusers_attention_prefix}.to_q.bias": checkpoint[
              f"{attention_prefix}.query.bias"
          ],
          # value
          f"{diffusers_attention_prefix}.to_v.weight": checkpoint[
              f"{attention_prefix}.value.weight"
          ],
          f"{diffusers_attention_prefix}.to_v.bias": checkpoint[
              f"{attention_prefix}.value.bias"
          ],
          # linear out
          f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[
              f"{attention_prefix}.proj.weight"
          ],
          f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[
              f"{attention_prefix}.proj.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 14 for calling a global function with example usage: transformer_attention_to_diffusers_checkpoint(checkpoint) and returns: dict