How to use the tf2rl.algos.ddpg.DDPG.get_argument function in tf2rl

def test_run_continuous(self):
        from tf2rl.algos.ddpg import DDPG
        parser = DDPG.get_argument(self.parser)
        parser.set_defaults(n_warmup=1)
        args, _ = parser.parse_known_args()

        def env_fn():
            return gym.make('Pendulum-v0')

        def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
            return DDPG(
                state_shape=env.observation_space.shape,
                action_dim=env.action_space.high.size,
                n_warmup=500,
                gpu=-1)

        def get_weights_fn(policy):
            return [policy.actor.weights,
                    policy.critic.weights,

def get_argument(parser=None):
        parser = DDPG.get_argument(parser)
        parser.add_argument('--eta', type=float, default=0.05)
        return parser

import argparse
import numpy as np
import gym
import roboschool

from tf2rl.algos.apex import apex_argument, run
from tf2rl.algos.ddpg import DDPG
from tf2rl.misc.target_update_ops import update_target_variables


if __name__ == '__main__':
    parser = apex_argument()
    parser.add_argument('--env-name', type=str,
                        default="RoboschoolAtlasForwardWalk-v1")
    parser = DDPG.get_argument(parser)
    args = parser.parse_args()

    # Prepare env and policy function
    def env_fn():
        return gym.make(args.env_name)

    def policy_fn(env, name, memory_capacity=int(1e6),
                  gpu=-1, noise_level=0.3):
        return DDPG(
            state_shape=env.observation_space.shape,
            action_dim=env.action_space.high.size,
            max_action=env.action_space.high[0],
            gpu=gpu,
            name=name,
            sigma=noise_level,
            batch_size=100,

import roboschool
import gym

from tf2rl.algos.ddpg import DDPG
from tf2rl.experiments.trainer import Trainer


if __name__ == '__main__':
    parser = Trainer.get_argument()
    parser = DDPG.get_argument(parser)
    parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
    parser.set_defaults(batch_size=100)
    parser.set_defaults(n_warmup=10000)
    args = parser.parse_args()

    env = gym.make(args.env_name)
    test_env = gym.make(args.env_name)
    policy = DDPG(
        state_shape=env.observation_space.shape,
        action_dim=env.action_space.high.size,
        gpu=args.gpu,
        memory_capacity=args.memory_capacity,
        max_action=env.action_space.high[0],
        batch_size=args.batch_size,
        n_warmup=args.n_warmup)
    trainer = Trainer(policy, env, args, test_env=test_env)