How to use the tf2rl.experiments.trainer.Trainer function in tf2rl

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 = BiResDDPG(
        state_shape=env.observation_space.shape,
        action_dim=env.action_space.high.size,
        gpu=args.gpu,
        eta=args.eta,
        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)
    trainer()

enable_categorical_dqn=args.enable_categorical_dqn,
        state_shape=env.observation_space.shape,
        action_dim=env.action_space.n,
        n_warmup=50000,
        target_replace_interval=10000,
        batch_size=32,
        memory_capacity=args.replay_buffer_size,
        discount=0.99,
        epsilon=1.,
        epsilon_min=0.1,
        epsilon_decay_step=int(1e6),
        optimizer=optimizer,
        update_interval=4,
        q_func=QFunc,
        gpu=args.gpu)
    trainer = Trainer(policy, env, args, test_env=test_env)
    trainer()

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 = SAC(
        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,
        auto_alpha=args.auto_alpha)
    trainer = Trainer(policy, env, args, test_env=test_env)
    trainer()

import time

import numpy as np
import tensorflow as tf

from tf2rl.misc.get_replay_buffer import get_replay_buffer
from tf2rl.experiments.trainer import Trainer


class IRLTrainer(Trainer):
    def __init__(
            self,
            policy,
            env,
            args,
            irl,
            expert_obs,
            expert_next_obs,
            expert_act,
            test_env=None):
        self._irl = irl
        args.dir_suffix = self._irl.policy_name + args.dir_suffix
        super().__init__(policy, env, args, test_env)
        # TODO: Add assertion to check dimention of expert demos and current policy, env is the same
        self._expert_obs = expert_obs
        self._expert_next_obs = expert_next_obs

def get_argument(parser=None):
        parser = Trainer.get_argument(parser)
        parser.add_argument('--expert-path-dir', default=None,
                            help='Path to directory that contains expert trajectories')
        return parser