code: def convert_open_clap_checkpoint(checkpoint):
      """
      Takes a state dict and returns a converted CLAP checkpoint.
      """
      # extract state dict for CLAP text embedding model, discarding the audio component
      model_state_dict = {}
      model_key = "clap.model."
      keys = list(checkpoint.keys())
      for key in keys:
          if key.startswith(model_key):
              model_state_dict[
                  key.replace(model_key, "")
              ] = checkpoint.get(key)
  
      new_checkpoint = {}
  
      sequential_layers_pattern = r".*sequential.(\d+).*"
      text_projection_pattern = r".*_projection.(\d+).*"
  
      for key, value in model_state_dict.items():
          # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end
          for key_to_ignore in CLAP_KEYS_TO_IGNORE:
              if key_to_ignore in key:
                  key = "spectrogram"
  
          # check if any key needs to be modified
          for (
              key_to_modify,
              new_key,
          ) in CLAP_KEYS_TO_MODIFY_MAPPING.items():
              if key_to_modify in key:
                  key = key.replace(key_to_modify, new_key)
  
          if re.match(sequential_layers_pattern, key):
              # replace sequential layers with list
              sequential_layer = re.match(
                  sequential_layers_pattern, key
              ).group(1)
  
              key = key.replace(
                  f"sequential.{sequential_layer}.",
                  f"layers.{int(sequential_layer)//3}.linear.",
              )
          elif re.match(text_projection_pattern, key):
              projecton_layer = int(
                  re.match(text_projection_pattern, key).group(1)
              )
  
              # Because in CLAP they use `nn.Sequential`...
              transformers_projection_layer = (
                  1 if projecton_layer == 0 else 2
              )
  
              key = key.replace(
                  f"_projection.{projecton_layer}.",
                  f"_projection.linear{transformers_projection_layer}.",
              )
  
          if "audio" and "qkv" in key:
              # split qkv into query key and value
              mixed_qkv = value
              qkv_dim = mixed_qkv.size(0) // 3
  
              query_layer = mixed_qkv[:qkv_dim]
              key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
              value_layer = mixed_qkv[qkv_dim * 2 :]
  
              new_checkpoint[key.replace("qkv", "query")] = query_layer
              new_checkpoint[key.replace("qkv", "key")] = key_layer
              new_checkpoint[key.replace("qkv", "value")] = value_layer
          elif key != "spectrogram":
              new_checkpoint[key] = value
  
      return new_checkpoint
  
imports: import argparse
  import re
  from typing import List, Union
  import torch
  import yaml
  from transformers import AutoFeatureExtractor, AutoTokenizer, ClapConfig, ClapModel, GPT2Config, GPT2Model, SpeechT5HifiGan, SpeechT5HifiGanConfig, T5Config, T5EncoderModel
  from diffusers import AudioLDM2Pipeline, AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel, AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import is_safetensors_available
  from diffusers.utils.import_utils import BACKENDS_MAPPING
label_desc: Use image node_id 13 for calling a global function with example usage: convert_open_clap_checkpoint(checkpoint) and returns: new_checkpoint