class_docstr: Basic DQN Model.
  
  >>> DQNLightning(env="CartPole-v1")  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  DQNLightning(
    (net): DQN(
      (net): Sequential(...)
    )
    (target_net): DQN(
      (net): Sequential(...)
    )
  )
docstr: Carries out several random steps through the environment to initially fill up the replay buffer with
  experiences.
  
  Args:
      steps: number of random steps to populate the buffer with
docstr_tok: ["Carries","out","several","random","steps","through","the","environment","to","initially","fill","up","the","replay","buffer","with","experiences",".","Args",":","steps",":","number","of","random","steps","to","populate","the","buffer","with"]
code: def populate(self, steps: int = 1000) -> None:
      """Carries out several random steps through the environment to initially fill up the replay buffer with
      experiences.
  
      Args:
          steps: number of random steps to populate the buffer with
  
      """
      for i in range(steps):
          self.agent.play_step(self.net, epsilon=1.0)
  
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 DQNLightning obj's underlying member method code with example usage: obj.populate(steps) without return types