class_docstr: implements both actor and critic in one model
docstr: forward of both actor and critic
docstr_tok: ["forward","of","both","actor","and","critic"]
code: def forward(self, x):
      """
      forward of both actor and critic
      """
      x = F.relu(self.affine1(x))
  
      # actor: choses action to take from state s_t
      # by returning probability of each action
      action_prob = F.softmax(self.action_head(x), dim=-1)
  
      # critic: evaluates being in the state s_t
      state_values = self.value_head(x)
  
      # return values for both actor and critic as a tuple of 2 values:
      # 1. a list with the probability of each action over the action space
      # 2. the value from state s_t
      return action_prob, state_values
  
file_path: ignite/examples/reinforcement_learning/actor_critic.py
imports: import argparse
  from collections import deque, namedtuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.distributions import Categorical
  from ignite.engine import Engine, Events
label_desc: Use image node_id 2 for calling the Policy obj's underlying member method code with example usage: obj.forward(x) and returns: action_prob,  state_values