code: def assign_attention_to_checkpoint(
      new_checkpoint, unet_state_dict, old_path, new_path, config
  ):
      qkv_weight = unet_state_dict.pop(f"{old_path}.qkv.weight")
      qkv_weight = qkv_weight[:, :, 0]
  
      qkv_bias = unet_state_dict.pop(f"{old_path}.qkv.bias")
  
      is_cross_attn_only = (
          "only_cross_attention" in config
          and config["only_cross_attention"]
      )
  
      split = 1 if is_cross_attn_only else 3
  
      weights, bias = split_attentions(
          weight=qkv_weight,
          bias=qkv_bias,
          split=split,
          chunk_size=config["attention_head_dim"],
      )
  
      if is_cross_attn_only:
          query_weight, q_bias = weights, bias
          new_checkpoint[f"{new_path}.to_q.weight"] = query_weight[0]
          new_checkpoint[f"{new_path}.to_q.bias"] = q_bias[0]
      else:
          [query_weight, key_weight, value_weight], [
              q_bias,
              k_bias,
              v_bias,
          ] = (weights, bias)
          new_checkpoint[f"{new_path}.to_q.weight"] = query_weight
          new_checkpoint[f"{new_path}.to_q.bias"] = q_bias
          new_checkpoint[f"{new_path}.to_k.weight"] = key_weight
          new_checkpoint[f"{new_path}.to_k.bias"] = k_bias
          new_checkpoint[f"{new_path}.to_v.weight"] = value_weight
          new_checkpoint[f"{new_path}.to_v.bias"] = v_bias
  
      encoder_kv_weight = unet_state_dict.pop(
          f"{old_path}.encoder_kv.weight"
      )
      encoder_kv_weight = encoder_kv_weight[:, :, 0]
  
      encoder_kv_bias = unet_state_dict.pop(
          f"{old_path}.encoder_kv.bias"
      )
  
      [encoder_k_weight, encoder_v_weight], [
          encoder_k_bias,
          encoder_v_bias,
      ] = split_attentions(
          weight=encoder_kv_weight,
          bias=encoder_kv_bias,
          split=2,
          chunk_size=config["attention_head_dim"],
      )
  
      new_checkpoint[f"{new_path}.add_k_proj.weight"] = encoder_k_weight
      new_checkpoint[f"{new_path}.add_k_proj.bias"] = encoder_k_bias
      new_checkpoint[f"{new_path}.add_v_proj.weight"] = encoder_v_weight
      new_checkpoint[f"{new_path}.add_v_proj.bias"] = encoder_v_bias
  
imports: import argparse
  import inspect
  import os
  import numpy
  import torch
  from torch.nn import functional
  from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer
  from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel
  from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker
label_desc: Use image node_id 12 for calling a global function with example usage: assign_attention_to_checkpoint(new_checkpoint, unet_state_dict, old_path, new_path, config) without return types