code: def forward(self, state):
      x = F.relu(self.fc(state))
      value = self.critic_linear2(x)
      policy_dist = F.softmax(self.actor_linear2(x))
  
      return value, policy_dist
  
imports: import math
  import random
  import logging
  import copy
  import torch
  from torch import optim
  from torch import nn
  import torch.nn.functional
  import numpy
  import nni
  from nni.tuner import Tuner
label_desc: Use image node_id 2 for calling the ActorCritic obj's underlying member method code with example usage: obj.forward(state) and returns: value,  policy_dist