code: def run_tts(lang, tts_text, speaker_audio_file):
      if XTTS_MODEL is None or not speaker_audio_file:
          return (
              "You need to run the previous step to load the model !!",
              None,
              None,
          )
  
      (
          gpt_cond_latent,
          speaker_embedding,
      ) = XTTS_MODEL.get_conditioning_latents(
          audio_path=speaker_audio_file,
          gpt_cond_len=XTTS_MODEL.config.gpt_cond_len,
          max_ref_length=XTTS_MODEL.config.max_ref_len,
          sound_norm_refs=XTTS_MODEL.config.sound_norm_refs,
      )
      out = XTTS_MODEL.inference(
          text=tts_text,
          language=lang,
          gpt_cond_latent=gpt_cond_latent,
          speaker_embedding=speaker_embedding,
          temperature=XTTS_MODEL.config.temperature,  # Add custom parameters here
          length_penalty=XTTS_MODEL.config.length_penalty,
          repetition_penalty=XTTS_MODEL.config.repetition_penalty,
          top_k=XTTS_MODEL.config.top_k,
          top_p=XTTS_MODEL.config.top_p,
      )
  
      with tempfile.NamedTemporaryFile(
          suffix=".wav", delete=False
      ) as fp:
          out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
          out_path = fp.name
          torchaudio.save(out_path, out["wav"], 24000)
  
      return "Speech generated !", out_path, speaker_audio_file
  
imports: import argparse
  import os
  import sys
  import tempfile
  import gradio
  import librosa.display
  import numpy
  import os
  import torch
  import torchaudio
  import traceback
  from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
  from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
  from TTS.tts.configs.xtts_config import XttsConfig
  from TTS.tts.models.xtts import Xtts
  import logging
label_desc: Use image node_id 3 for calling a global function with example usage: run_tts(lang, tts_text, speaker_audio_file) and returns: str,  out_path,  speaker_audio_file, str,  None,  None