code: def conv_attn_to_linear(checkpoint):
      keys = list(checkpoint.keys())
      attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"]
      proj_key = "to_out.0.weight"
      for key in keys:
          if (
              ".".join(key.split(".")[-2:]) in attn_keys
              or ".".join(key.split(".")[-3:]) == proj_key
          ):
              if checkpoint[key].ndim > 2:
                  checkpoint[key] = checkpoint[key].squeeze()
  
imports: import argparse
  import re
  import torch
  from transformers import AutoFeatureExtractor, AutoTokenizer, ClapConfig, ClapModel, SpeechT5HifiGan, SpeechT5HifiGanConfig
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, MusicLDMPipeline, PNDMScheduler, UNet2DConditionModel
  from diffusers.utils import is_omegaconf_available
  from diffusers.utils.import_utils import BACKENDS_MAPPING
label_desc: Use image node_id 7 for calling a global function with example usage: conv_attn_to_linear(checkpoint) without return types