code: def assign_to_checkpoint(
      paths,
      checkpoint,
      old_checkpoint,
      attention_paths_to_split=None,
      additional_replacements=None,
      config=None,
  ):
      """
      This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
      attention layers, and takes into account additional replacements that may arise.
  
      Assigns the weights to the new checkpoint.
      """
      assert isinstance(
          paths, list
      ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
              old_tensor = old_checkpoint[path]
              channels = old_tensor.shape[0] // 3
  
              target_shape = (
                  (-1, channels) if len(old_tensor.shape) == 3 else (-1)
              )
  
              num_heads = (
                  old_tensor.shape[0]
                  // config["num_head_channels"]
                  // 3
              )
  
              old_tensor = old_tensor.reshape(
                  (num_heads, 3 * channels // num_heads)
                  + old_tensor.shape[1:]
              )
              query, key, value = old_tensor.split(
                  channels // num_heads, dim=1
              )
  
              checkpoint[path_map["query"]] = query.reshape(
                  target_shape
              )
              checkpoint[path_map["key"]] = key.reshape(target_shape)
              checkpoint[path_map["value"]] = value.reshape(
                  target_shape
              )
  
      for path in paths:
          new_path = path["new"]
  
          # These have already been assigned
          if (
              attention_paths_to_split is not None
              and new_path in attention_paths_to_split
          ):
              continue
  
          # Global renaming happens here
          new_path = new_path.replace(
              "middle_block.0", "mid_block.resnets.0"
          )
          new_path = new_path.replace(
              "middle_block.1", "mid_block.attentions.0"
          )
          new_path = new_path.replace(
              "middle_block.2", "mid_block.resnets.1"
          )
  
          if additional_replacements is not None:
              for replacement in additional_replacements:
                  new_path = new_path.replace(
                      replacement["old"], replacement["new"]
                  )
  
          # proj_attn.weight has to be converted from conv 1D to linear
          is_attn_weight = "proj_attn.weight" in new_path or (
              "attentions" in new_path and "to_" in new_path
          )
          shape = old_checkpoint[path["old"]].shape
          if is_attn_weight and len(shape) == 3:
              checkpoint[new_path] = old_checkpoint[path["old"]][
                  :, :, 0
              ]
          elif is_attn_weight and len(shape) == 4:
              checkpoint[new_path] = old_checkpoint[path["old"]][
                  :, :, 0, 0
              ]
          else:
              checkpoint[new_path] = old_checkpoint[path["old"]]
  
imports: import re
  from contextlib import nullcontext
  from io import BytesIO
  from typing import Dict, Optional, Union
  import requests
  import torch
  import yaml
  from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection
  from ...models import AutoencoderKL, ControlNetModel, PriorTransformer, UNet2DConditionModel
  from ...schedulers import DDIMScheduler, DDPMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, UnCLIPScheduler
  from ...utils import is_accelerate_available, logging
  from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
  from ..paint_by_example import PaintByExampleImageEncoder
  from ..pipeline_utils import DiffusionPipeline
  from .safety_checker import StableDiffusionSafetyChecker
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
label_desc: Use image node_id 6 for calling a global function with example usage: assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_paths_to_split, additional_replacements, config) without return types