class_docstr: Policy network, for continuous action spaces, which returns a distribution and an action given an
  observation.
docstr: Args:
      input_shape: observation shape of the environment
      n_actions: number of discrete actions available in the environment
code: def __init__(self, actor_net, act_dim):
      """
      Args:
          input_shape: observation shape of the environment
          n_actions: number of discrete actions available in the environment
      """
      super().__init__()
      self.actor_net = actor_net
      log_std = -0.5 * torch.ones(act_dim, dtype=torch.float)
      self.log_std = nn.Parameter(log_std)
  
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 1 to create a new ActorContinuous object from inherited base classes: nn with example: obj = ActorContinuous(actor_net, act_dim)