code: def save_checkpoint(
      model, optimizer, current_step, epoch, r, output_folder, **kwargs
  ):
      """Save model checkpoint, intended for saving checkpoints at training.
  
      Args:
          model (TTS.tts.models.Model): models object to be saved.
          optimizer (torch.optim.optimizers.Optimizer): model optimizer used for training.
          current_step (int): current number of training steps.
          epoch (int): current number of training epochs.
          r (int): model reduction rate for Tacotron models.
          output_path (str): output path to save the model file.
      """
      file_name = "checkpoint_{}.pth.tar".format(current_step)
      checkpoint_path = os.path.join(output_folder, file_name)
      print(" > CHECKPOINT : {}".format(checkpoint_path))
      save_model(
          model,
          optimizer,
          current_step,
          epoch,
          r,
          checkpoint_path,
          **kwargs,
      )
  
file_path: TTS/TTS/tts/utils/io.py
imports: import os
  import torch
  import datetime
  import pickle
  from TTS.utils.io import RenamingUnpickler
label_desc: Use image node_id 3 for calling a global function with example usage: save_checkpoint(model, optimizer, current_step, epoch, r, output_folder) without return types