code: def create_transformers_vocoder_config(original_config):
      """
      Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.
      """
      vocoder_params = original_config["model"]["params"][
          "vocoder_config"
      ]["params"]
  
      config = {
          "model_in_dim": vocoder_params["num_mels"],
          "sampling_rate": vocoder_params["sampling_rate"],
          "upsample_initial_channel": vocoder_params[
              "upsample_initial_channel"
          ],
          "upsample_rates": list(vocoder_params["upsample_rates"]),
          "upsample_kernel_sizes": list(
              vocoder_params["upsample_kernel_sizes"]
          ),
          "resblock_kernel_sizes": list(
              vocoder_params["resblock_kernel_sizes"]
          ),
          "resblock_dilation_sizes": [
              list(resblock_dilation)
              for resblock_dilation in vocoder_params[
                  "resblock_dilation_sizes"
              ]
          ],
          "normalize_before": False,
      }
  
      return config
  
imports: import argparse
  import re
  import torch
  import yaml
  from transformers import AutoFeatureExtractor, AutoTokenizer, ClapConfig, ClapModel, SpeechT5HifiGan, SpeechT5HifiGanConfig
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LMSDiscreteScheduler, MusicLDMPipeline, PNDMScheduler, UNet2DConditionModel
label_desc: Use image node_id 14 for calling a global function with example usage: create_transformers_vocoder_config(original_config) and returns: config