code: def parse_args():
      parser = argparse.ArgumentParser(
          "DQN experiments for Atari games"
      )
      # Environment
      parser.add_argument(
          "--env", type=str, default="Pong", help="name of the game"
      )
      parser.add_argument(
          "--seed", type=int, default=42, help="which seed to use"
      )
      # Core DQN parameters
      parser.add_argument(
          "--replay-buffer-size",
          type=int,
          default=int(1e6),
          help="replay buffer size",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1e-4,
          help="learning rate for Adam optimizer",
      )
      parser.add_argument(
          "--num-steps",
          type=int,
          default=int(2e8),
          help="total number of steps to \
                          run the environment for",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=32,
          help="number of transitions to optimize \
                          at the same time",
      )
      parser.add_argument(
          "--learning-freq",
          type=int,
          default=4,
          help="number of iterations between \
                          every optimization step",
      )
      parser.add_argument(
          "--target-update-freq",
          type=int,
          default=40000,
          help="number of iterations between \
                          every target network update",
      )
      # Bells and whistles
      boolean_flag(
          parser,
          "noisy",
          default=False,
          help="whether or not to NoisyNetwork",
      )
      boolean_flag(
          parser,
          "double-q",
          default=True,
          help="whether or not to use double q learning",
      )
      boolean_flag(
          parser,
          "dueling",
          default=False,
          help="whether or not to use dueling model",
      )
      boolean_flag(
          parser,
          "prioritized",
          default=False,
          help="whether or not to use prioritized replay buffer",
      )
      parser.add_argument(
          "--prioritized-alpha",
          type=float,
          default=0.6,
          help="alpha parameter for prioritized replay buffer",
      )
      parser.add_argument(
          "--prioritized-beta0",
          type=float,
          default=0.4,
          help="initial value of beta \
                          parameters for prioritized replay",
      )
      parser.add_argument(
          "--prioritized-eps",
          type=float,
          default=1e-6,
          help="eps parameter for prioritized replay buffer",
      )
      # Checkpointing
      parser.add_argument(
          "--save-dir",
          type=str,
          default=None,
          required=True,
          help="directory in which \
                          training state and model should be saved.",
      )
      parser.add_argument(
          "--save-azure-container",
          type=str,
          default=None,
          help="It present data will saved/loaded from Azure. \
                          Should be in format ACCOUNT_NAME:ACCOUNT_KEY:\
                          CONTAINER",
      )
      parser.add_argument(
          "--save-freq",
          type=int,
          default=1e6,
          help="save model once every time this many \
                          iterations are completed",
      )
      boolean_flag(
          parser,
          "load-on-start",
          default=True,
          help="if true and model was previously saved then training \
                   will be resumed",
      )
  
      # V: Attack Arguments #
      parser.add_argument(
          "--attack",
          type=str,
          default=None,
          help="Method to attack the model.",
      )
      parser.add_argument(
          "--attack-init",
          type=int,
          default=0,
          help="Iteration no. to begin attacks",
      )
      parser.add_argument(
          "--attack-prob",
          type=float,
          default=0.0,
          help="Probability of attack at each step, \
                          float in range 0 - 1.0",
      )
      return parser.parse_args()
  
imports: import argparse
  import gym
  import numpy
  import os
  import tensorflow
  import tempfile
  import time
  import json
  import random
  import rlattack.common.tf_util
  from rlattack import logger
  from rlattack import deepq
  from rlattack.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer
  from rlattack.common.misc_util import boolean_flag, pickle_load, pretty_eta, relatively_safe_pickle_dump, set_global_seeds, RunningAvg, SimpleMonitor
  from rlattack.common.schedules import LinearSchedule, PiecewiseSchedule
  from rlattack.common.atari_wrappers_deprecated import wrap_dqn
  from rlattack.common.azure_utils import Container
  from model import model, dueling_model
  from statistics import statistics
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser