code: def train_gpt(
      language,
      num_epochs,
      batch_size,
      grad_acumm,
      train_csv,
      eval_csv,
      output_path,
      max_audio_length=255995,
  ):
      #  Logging parameters
      RUN_NAME = "GPT_XTTS_FT"
      PROJECT_NAME = "XTTS_trainer"
      DASHBOARD_LOGGER = "tensorboard"
      LOGGER_URI = None
  
      # Set here the path that the checkpoints will be saved. Default: ./run/training/
      OUT_PATH = os.path.join(output_path, "run", "training")
  
      # Training Parameters
      OPTIMIZER_WD_ONLY_ON_WEIGHTS = (
          True  # for multi-gpu training please make it False
      )
      START_WITH_EVAL = False  # if True it will star with evaluation
      BATCH_SIZE = batch_size  # set here the batch size
      GRAD_ACUMM_STEPS = (
          grad_acumm  # set here the grad accumulation steps
      )
  
      # Define here the dataset that you want to use for the fine-tuning on.
      config_dataset = BaseDatasetConfig(
          formatter="coqui",
          dataset_name="ft_dataset",
          path=os.path.dirname(train_csv),
          meta_file_train=train_csv,
          meta_file_val=eval_csv,
          language=language,
      )
  
      # Add here the configs of the datasets
      DATASETS_CONFIG_LIST = [config_dataset]
  
      # Define the path where XTTS v2.0.1 files will be downloaded
      CHECKPOINTS_OUT_PATH = os.path.join(
          OUT_PATH, "XTTS_v2.0_original_model_files/"
      )
      os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
  
      # DVAE files
      DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
      MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
  
      # Set the path to the downloaded files
      DVAE_CHECKPOINT = os.path.join(
          CHECKPOINTS_OUT_PATH, os.path.basename(DVAE_CHECKPOINT_LINK)
      )
      MEL_NORM_FILE = os.path.join(
          CHECKPOINTS_OUT_PATH, os.path.basename(MEL_NORM_LINK)
      )
  
      # download DVAE files if needed
      if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(
          MEL_NORM_FILE
      ):
          print(" > Downloading DVAE files!")
          ModelManager._download_model_files(
              [MEL_NORM_LINK, DVAE_CHECKPOINT_LINK],
              CHECKPOINTS_OUT_PATH,
              progress_bar=True,
          )
  
      # Download XTTS v2.0 checkpoint if needed
      TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
      XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth"
      XTTS_CONFIG_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/config.json"
  
      # XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
      TOKENIZER_FILE = os.path.join(
          CHECKPOINTS_OUT_PATH, os.path.basename(TOKENIZER_FILE_LINK)
      )  # vocab.json file
      XTTS_CHECKPOINT = os.path.join(
          CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CHECKPOINT_LINK)
      )  # model.pth file
      XTTS_CONFIG_FILE = os.path.join(
          CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CONFIG_LINK)
      )  # config.json file
  
      # download XTTS v2.0 files if needed
      if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(
          XTTS_CHECKPOINT
      ):
          print(" > Downloading XTTS v2.0 files!")
          ModelManager._download_model_files(
              [
                  TOKENIZER_FILE_LINK,
                  XTTS_CHECKPOINT_LINK,
                  XTTS_CONFIG_LINK,
              ],
              CHECKPOINTS_OUT_PATH,
              progress_bar=True,
          )
  
      # init args and config
      model_args = GPTArgs(
          max_conditioning_length=132300,  # 6 secs
          min_conditioning_length=66150,  # 3 secs
          debug_loading_failures=False,
          max_wav_length=max_audio_length,  # ~11.6 seconds
          max_text_length=200,
          mel_norm_file=MEL_NORM_FILE,
          dvae_checkpoint=DVAE_CHECKPOINT,
          xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
          tokenizer_file=TOKENIZER_FILE,
          gpt_num_audio_tokens=1026,
          gpt_start_audio_token=1024,
          gpt_stop_audio_token=1025,
          gpt_use_masking_gt_prompt_approach=True,
          gpt_use_perceiver_resampler=True,
      )
      # define audio config
      audio_config = XttsAudioConfig(
          sample_rate=22050,
          dvae_sample_rate=22050,
          output_sample_rate=24000,
      )
      # training parameters config
      config = GPTTrainerConfig(
          epochs=num_epochs,
          output_path=OUT_PATH,
          model_args=model_args,
          run_name=RUN_NAME,
          project_name=PROJECT_NAME,
          run_description="""
              GPT XTTS training
              """,
          dashboard_logger=DASHBOARD_LOGGER,
          logger_uri=LOGGER_URI,
          audio=audio_config,
          batch_size=BATCH_SIZE,
          batch_group_size=48,
          eval_batch_size=BATCH_SIZE,
          num_loader_workers=8,
          eval_split_max_size=256,
          print_step=50,
          plot_step=100,
          log_model_step=100,
          save_step=1000,
          save_n_checkpoints=1,
          save_checkpoints=True,
          # target_loss="loss",
          print_eval=False,
          # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
          optimizer="AdamW",
          optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
          optimizer_params={
              "betas": [0.9, 0.96],
              "eps": 1e-8,
              "weight_decay": 1e-2,
          },
          lr=5e-06,  # learning rate
          lr_scheduler="MultiStepLR",
          # it was adjusted accordly for the new step scheme
          lr_scheduler_params={
              "milestones": [50000 * 18, 150000 * 18, 300000 * 18],
              "gamma": 0.5,
              "last_epoch": -1,
          },
          test_sentences=[],
      )
  
      # init the model from config
      model = GPTTrainer.init_from_config(config)
  
      # load training samples
      train_samples, eval_samples = load_tts_samples(
          DATASETS_CONFIG_LIST,
          eval_split=True,
          eval_split_max_size=config.eval_split_max_size,
          eval_split_size=config.eval_split_size,
      )
  
      # init the trainer and 🚀
      trainer = Trainer(
          TrainerArgs(
              restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
              skip_train_epoch=False,
              start_with_eval=START_WITH_EVAL,
              grad_accum_steps=GRAD_ACUMM_STEPS,
          ),
          config,
          output_path=OUT_PATH,
          model=model,
          train_samples=train_samples,
          eval_samples=eval_samples,
      )
      trainer.fit()
  
      # get the longest text audio file to use as speaker reference
      samples_len = [
          len(item["text"].split(" ")) for item in train_samples
      ]
      longest_text_idx = samples_len.index(max(samples_len))
      speaker_ref = train_samples[longest_text_idx]["audio_file"]
  
      trainer_out_path = trainer.output_path
  
      # deallocate VRAM and RAM
      del model, trainer, train_samples, eval_samples
      gc.collect()
  
      return (
          XTTS_CONFIG_FILE,
          XTTS_CHECKPOINT,
          TOKENIZER_FILE,
          trainer_out_path,
          speaker_ref,
      )
  
imports: import os
  import gc
  from trainer import Trainer, TrainerArgs
  from TTS.config.shared_configs import BaseDatasetConfig
  from TTS.tts.datasets import load_tts_samples
  from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
  from TTS.utils.manage import ModelManager
label_desc: Use image node_id 1 for calling a global function with example usage: train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path, max_audio_length) and returns: XTTS_CONFIG_FILE,  XTTS_CHECKPOINT,  TOKENIZER_FILE,  trainer_out_path,  speaker_ref