code: def auto_train(
      data_path,
      save_path="model.pth",
      load_model: str = None,
      save_epochs=1,
  ):
      data_x, data_y = [], []
  
      if load_model and os.path.isfile(load_model):
          print("Loading model from", load_model)
          model_training = HubertTokenizer.load_from_checkpoint(
              load_model, "cuda"
          )
      else:
          print("Creating new model.")
          model_training = HubertTokenizer(version=1).to(
              "cuda"
          )  # Settings for the model to run without lstm
      save_path = os.path.join(data_path, save_path)
      base_save_path = ".".join(save_path.split(".")[:-1])
  
      sem_string = "_semantic.npy"
      feat_string = "_semantic_features.npy"
  
      ready = os.path.join(data_path, "ready")
      for input_file in os.listdir(ready):
          full_path = os.path.join(ready, input_file)
          if input_file.endswith(sem_string):
              data_y.append(numpy.load(full_path))
          elif input_file.endswith(feat_string):
              data_x.append(numpy.load(full_path))
      model_training.prepare_training()
  
      epoch = 1
  
      while 1:
          for _ in range(save_epochs):
              j = 0
              for x, y in zip(data_x, data_y):
                  model_training.train_step(
                      torch.tensor(x).to("cuda"),
                      torch.tensor(y).to("cuda"),
                      j % 50 == 0,
                  )  # Print loss every 50 steps
                  j += 1
          save_p = save_path
          save_p_2 = f"{base_save_path}_epoch_{epoch}.pth"
          model_training.save(save_p)
          model_training.save(save_p_2)
          print(f"Epoch {epoch} completed")
          epoch += 1
  
file_path: TTS/TTS/tts/layers/bark/hubert/tokenizer.py
imports: import json
  import os.path
  from zipfile import ZipFile
  import numpy
  import torch
  from torch import nn, optim
label_desc: Use image node_id 1 for calling a global function with example usage: auto_train(data_path, save_path, load_model, save_epochs) without return types