code: def __init__(
      self,
      inputs_tf,
      dimo,
      dimg,
      dimu,
      max_u,
      o_stats,
      g_stats,
      hidden,
      layers,
      **kwargs,
  ):
      """The actor-critic network and related training code.
  
      Args:
          inputs_tf (dict of tensors): all necessary inputs for the network: the
              observation (o), the goal (g), and the action (u)
          dimo (int): the dimension of the observations
          dimg (int): the dimension of the goals
          dimu (int): the dimension of the actions
          max_u (float): the maximum magnitude of actions; action outputs will be scaled
              accordingly
          o_stats (baselines.her.Normalizer): normalizer for observations
          g_stats (baselines.her.Normalizer): normalizer for goals
          hidden (int): number of hidden units that should be used in hidden layers
          layers (int): number of hidden layers
      """
      self.o_tf = inputs_tf["o"]
      self.g_tf = inputs_tf["g"]
      self.u_tf = inputs_tf["u"]
  
      # Prepare inputs for actor and critic.
      o = self.o_stats.normalize(self.o_tf)
      g = self.g_stats.normalize(self.g_tf)
      input_pi = tf.concat(axis=1, values=[o, g])  # for actor
  
      # Networks.
      with tf.variable_scope("pi"):
          self.pi_tf = self.max_u * tf.tanh(
              nn(input_pi, [self.hidden] * self.layers + [self.dimu])
          )
      with tf.variable_scope("Q"):
          # for policy training
          input_Q = tf.concat(
              axis=1, values=[o, g, self.pi_tf / self.max_u]
          )
          self.Q_pi_tf = nn(input_Q, [self.hidden] * self.layers + [1])
          # for critic training
          input_Q = tf.concat(
              axis=1, values=[o, g, self.u_tf / self.max_u]
          )
          self._input_Q = input_Q  # exposed for tests
          self.Q_tf = nn(
              input_Q, [self.hidden] * self.layers + [1], reuse=True
          )
  
imports: import tensorflow
  from baselines.her.util import store_args, nn
label_desc: Use image node_id 1 to create a new ActorCritic object  with example: obj = ActorCritic(inputs_tf, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, layers)