class_docstr: Simple MLP network.
  
  >>> DQN(10, 5)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  DQN(
    (net): Sequential(...)
  )
code: def forward(self, x):
      return self.net(x.float())
  
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 2 for calling the DQN obj's underlying member method code with example usage: obj.forward(x) and returns: self