code: def init_train_args(self, parser):
          '''Only invoked conditionally if subcommand is 'train' '''
          parser.add_argument('-f', '--configuration', dest='config', default=DEFAULT_USER_CONFIG_PATH,
                              help='the path to the configuration file to use -- ./config.yaml by default')
          parser.add_argument('-c', '--dataset-name', help='the name of the saved dataset to train on')
          parser.add_argument('-s', '--saved-model-name', help='the name of the saved model to continue training')
          parser.add_argument('-n', '--new-model-name', help='the name to give the newly trained model')
          parser.add_argument('-g', '--gpus', dest='num_gpus', type=int, default=1, help='the number of gpus to use during training'f init_train_args(self, parser):
          '''Only invoked conditionally if subcommand is 'train' '''
          parser.add_argument('-f', '--configuration', dest='config', default=DEFAULT_USER_CONFIG_PATH,
                              help='the path to the configuration file to use -- ./config.yaml by default')
          parser.add_argument('-c', '--dataset-name', help='the name of the saved dataset to train on')
          parser.add_argument('-s', '--saved-model-name', help='the name of the saved model to continue training')
          parser.add_argument('-n', '--new-model-name', help='the name to give the newly trained model')
          parser.add_argument('-g', '--gpus', dest='num_gpus', type=int, default=1, help='the number of gpus to use during training')
docs: Only invoked conditionally if subcommand is 'train'