code: def vqvae_attention_to_diffusers_checkpoint(
      checkpoint, *, diffusers_attention_prefix, attention_prefix
  ):
      return {
          # group_norm
          f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[
              f"{attention_prefix}.norm.weight"
          ],
          f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[
              f"{attention_prefix}.norm.bias"
          ],
          # query
          f"{diffusers_attention_prefix}.query.weight": checkpoint[
              f"{attention_prefix}.q.weight"
          ][:, :, 0, 0],
          f"{diffusers_attention_prefix}.query.bias": checkpoint[
              f"{attention_prefix}.q.bias"
          ],
          # key
          f"{diffusers_attention_prefix}.key.weight": checkpoint[
              f"{attention_prefix}.k.weight"
          ][:, :, 0, 0],
          f"{diffusers_attention_prefix}.key.bias": checkpoint[
              f"{attention_prefix}.k.bias"
          ],
          # value
          f"{diffusers_attention_prefix}.value.weight": checkpoint[
              f"{attention_prefix}.v.weight"
          ][:, :, 0, 0],
          f"{diffusers_attention_prefix}.value.bias": checkpoint[
              f"{attention_prefix}.v.bias"
          ],
          # proj_attn
          f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[
              f"{attention_prefix}.proj_out.weight"
          ][:, :, 0, 0],
          f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[
              f"{attention_prefix}.proj_out.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 10 for calling a global function with example usage: vqvae_attention_to_diffusers_checkpoint(checkpoint) and returns: dict