code: def argsparser():
      parser = argparse.ArgumentParser(
          "Tensorflow Implementation of GAIL"
      )
      parser.add_argument(
          "--env_id", help="environment ID", default="Hopper-v2"
      )
      parser.add_argument(
          "--seed", help="RNG seed", type=int, default=0
      )
      parser.add_argument(
          "--expert_path",
          type=str,
          default="data/deterministic.trpo.Hopper.0.00.npz",
      )
      parser.add_argument(
          "--checkpoint_dir",
          help="the directory to save model",
          default="checkpoint",
      )
      parser.add_argument(
          "--log_dir",
          help="the directory to save log file",
          default="log",
      )
      parser.add_argument(
          "--load_model_path",
          help="if provided, load the model",
          type=str,
          default=None,
      )
      # Task
      parser.add_argument(
          "--task",
          type=str,
          choices=["train", "evaluate", "sample"],
          default="train",
      )
      # for evaluatation
      boolean_flag(
          parser,
          "stochastic_policy",
          default=False,
          help="use stochastic/deterministic policy to evaluate",
      )
      boolean_flag(
          parser,
          "save_sample",
          default=False,
          help="save the trajectories or not",
      )
      #  Mujoco Dataset Configuration
      parser.add_argument("--traj_limitation", type=int, default=-1)
      # Optimization Configuration
      parser.add_argument(
          "--g_step",
          help="number of steps to train policy in each epoch",
          type=int,
          default=3,
      )
      parser.add_argument(
          "--d_step",
          help="number of steps to train discriminator in each epoch",
          type=int,
          default=1,
      )
      # Network Configuration (Using MLP Policy)
      parser.add_argument("--policy_hidden_size", type=int, default=100)
      parser.add_argument(
          "--adversary_hidden_size", type=int, default=100
      )
      # Algorithms Configuration
      parser.add_argument(
          "--algo", type=str, choices=["trpo", "ppo"], default="trpo"
      )
      parser.add_argument("--max_kl", type=float, default=0.01)
      parser.add_argument(
          "--policy_entcoeff",
          help="entropy coefficiency of policy",
          type=float,
          default=0,
      )
      parser.add_argument(
          "--adversary_entcoeff",
          help="entropy coefficiency of discriminator",
          type=float,
          default=1e-3,
      )
      # Traing Configuration
      parser.add_argument(
          "--save_per_iter",
          help="save model every xx iterations",
          type=int,
          default=100,
      )
      parser.add_argument(
          "--num_timesteps",
          help="number of timesteps per episode",
          type=int,
          default=5e6,
      )
      # Behavior Cloning
      boolean_flag(
          parser, "pretrained", default=False, help="Use BC to pretrain"
      )
      parser.add_argument(
          "--BC_max_iter",
          help="Max iteration for training BC",
          type=int,
          default=1e4,
      )
      return parser.parse_args()
  
file_path: baselines/baselines/gail/run_mujoco.py
imports: import argparse
  import os.path
  import logging
  from mpi4py import MPI
  from tqdm import tqdm
  import numpy
  import gym
  from baselines.gail import mlp_policy
  from baselines.common import set_global_seeds, tf_util
  from baselines.common.misc_util import boolean_flag
  from baselines import bench
  from baselines import logger
  from baselines.gail.dataset.mujoco_dset import Mujoco_Dset
  from baselines.gail.adversary import TransitionClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: argsparser() and returns: parser