class_docstr: Basic DQN Model.
  
  >>> DQNLightning(env="CartPole-v1")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  DQNLightning(
    (net): DQN(
      (net): Sequential(...)
    )
    (target_net): DQN(
      (net): Sequential(...)
    )
  )
docstr: Passes in a state `x` through the network and gets the `q_values` of each action as an output.
  
  Args:
      x: environment state
  
  Returns:
      q values
docstr_tok: ["Passes","in","a","state","`","x","`","through","the","network","and","gets","the","`","q_values","`","of","each","action","as","an","output",".","Args",":","x",":","environment","state","Returns",":","q","values"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Passes in a state `x` through the network and gets the `q_values` of each action as an output.
  
      Args:
          x: environment state
  
      Returns:
          q values
  
      """
      return self.net(x)
  
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 3 for calling the DQNLightning obj's underlying member method code with example usage: obj.forward(x) and returns: self