class_docstr: Basic DQN Model.
  
  >>> DQNLightning(env="CartPole-v1")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  DQNLightning(
    (net): DQN(
      (net): Sequential(...)
    )
    (target_net): DQN(
      (net): Sequential(...)
    )
  )
docstr: Retrieve device currently being used by minibatch.
docstr_tok: ["Retrieve","device","currently","being","used","by","minibatch","."]
code: def get_device(self, batch) -> str:
      """Retrieve device currently being used by minibatch."""
      return batch[0].device.index if self.on_gpu else "cpu"
  
imports: import argparse
  from collections import OrderedDict, deque, namedtuple
  from typing import Iterator, List, Tuple
  import gym
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import IterableDataset
label_desc: Use image node_id 9 for calling the DQNLightning obj's underlying member method code with example usage: obj.get_device(batch) and returns: unknown