class_docstr: implements both actor and critic in one model
code: def __init__(self):
      super(Policy, self).__init__()
      self.affine1 = nn.Linear(4, 128)
  
      # actor's layer
      self.action_head = nn.Linear(128, 2)
  
      # critic's layer
      self.value_head = nn.Linear(128, 1)
  
      # action & reward buffer
      self.saved_actions = []
      self.rewards = []
  
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 1 to create a new Policy object from inherited base classes: nn with example: obj = Policy()