class_docstr: Neural Network to approximate v value.
  Args:
      obs_dim (int): Dimension of observation space.
      act_dim (int): Dimension of action space.
class_docstr_tok: ["Neural","Network","to","approximate","v","value",".","Args",":","obs_dim","(","int",")",":","Dimension","of","observation","space",".","act_dim","(","int",")",":","Dimension","of","action","space","."]
code: def __init__(self, obs_dim, act_dim):
      super().__init__()
  
      self.obs_dim = obs_dim
      self.act_dim = act_dim
  
      self.l1 = nn.Linear(obs_dim, 16)
      self.l2 = nn.Linear(144, 24)
      self.l3 = nn.Linear(40, 128)
      self.l4 = nn.Linear(128, act_dim)
  
      self.network = nn.Sequential(
          nn.Conv2d(
              in_channels=5,
              out_channels=16,
              kernel_size=(3, 3),
              stride=2,
          ),
          nn.ReLU(),
          nn.Conv2d(
              in_channels=16,
              out_channels=16,
              kernel_size=(3, 3),
              stride=2,
          ),
          nn.ReLU(),
          nn.Conv2d(
              in_channels=16,
              out_channels=16,
              kernel_size=(2, 2),
              stride=1,
          ),
          nn.ReLU(),
      )
  
imports: import pickle
  import base64
  import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Categorical
  from zerosum_env.envs.halite.helpers import *
  from zerosum_env import make, evaluate
  from collections import deque
  import os
label_desc: Use image node_id 1 to create a new Actor object from inherited base classes: nn with example: obj = Actor(obs_dim, act_dim)