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","."]
docstr: Sampling action
  Args:
      state (np.array): representation of current state 
code: def sample(self, state):
      """Sampling action
      Args:
          state (np.array): representation of current state
      """
  
      state_tensor = torch.tensor(state, dtype=torch.float32)
      probs = self(state_tensor)
      dist = Categorical(probs)
      action = dist.sample().detach().numpy()
      return action
  
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 4 for calling the Actor obj's underlying member method code with example usage: obj.sample(state) and returns: action