class_docstr: Policy network, for continuous action spaces, which returns a distribution and an action given an
  observation.
code: def forward(self, states):
      mu = self.actor_net(states)
      std = torch.exp(self.log_std)
      pi = Normal(loc=mu, scale=std)
      actions = pi.sample()
  
      return pi, actions
  
imports: import argparse
  from typing import Callable, Iterator, List, Tuple
  import gym
  import torch
  from lightning.pytorch import LightningModule, Trainer, cli_lightning_logo, seed_everything
  from torch import nn
  from torch.distributions import Categorical, Normal
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader, IterableDataset
label_desc: Use image node_id 2 for calling the ActorContinuous obj's underlying member method code with example usage: obj.forward(states) and returns: pi,  actions