text: import argparse
  import os
  from datetime import datetime
  
  import torch
  from torchvision import models
  
  
  def fit(model: torch.nn.modules.Module) -> None:
      # Write some training codes...
      pass
  
  
  def save(model: torch.nn.modules.Module, path: str) -> None:
      """ Save a model.
  
      Args:
          model (torch.nn.modules.Module): A model to be saved
          path: A directory in which a model is saved
      """
  
      suffix = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
      path = os.path.join(path, f'model-{suffix}.pt')
      # If you use `model.state_dict()`, SageMaker compilation will fail.
      torch.save(model, path)
  
  
  def parse_args() -> argparse.Namespace:
      """ Get arguments
  
      Mainly used to get the following arguments. <br/>
      See https://sagemaker.readthedocs.io/en/stable/overview.html#prepare-a-training-script
  
      Returns:
           argparse.Namespace: Arguments
      """
  
      parser = argparse.ArgumentParser()
  
      # hyperparameters sent by the client are passed as command-line arguments to the script.
  
      # input data and model directories
      parser.add_argument('--model_dir', type=str)
      parser.add_argument('--sm_model_dir', type=str, default=os.environ.get('SM_MODEL_DIR'))
      parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))
      parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))
  
      args, _ = parser.parse_known_args()
      return args
  
  
  if __name__ == '__main__':
      args = parse_args()
      vgg16 = models.vgg16(pretrained=True)
      fit(vgg16)
      save(vgg16, args.sm_model_dir)
  