docstr: build the graph
  
  :param obs_ph: (tf.Tensor) the observation placeholder
  :param acs_ph: (tf.Tensor) the action placeholder
  :param reuse: (bool)
  :return: (tf.Tensor) the graph output
docstr_tok: ["build","the","graph",":","param","obs_ph",":","(","tf.Tensor",")","the","observation","placeholder",":","param","acs_ph",":","(","tf.Tensor",")","the","action","placeholder",":","param","reuse",":","(","bool",")",":","return",":","(","tf.Tensor",")","the","graph","output"]
code: def build_graph(self, obs_ph, acs_ph, reuse=False):
      """
      build the graph
  
      :param obs_ph: (tf.Tensor) the observation placeholder
      :param acs_ph: (tf.Tensor) the action placeholder
      :param reuse: (bool)
      :return: (tf.Tensor) the graph output
      """
      with tf.variable_scope(self.scope):
          if reuse:
              tf.get_variable_scope().reuse_variables()
  
          if self.normalize:
              with tf.variable_scope("obfilter"):
                  self.obs_rms = RunningMeanStd(
                      shape=self.observation_shape
                  )
              obs = (obs_ph - self.obs_rms.mean) / self.obs_rms.std
          else:
              obs = obs_ph
  
          if self.discrete_actions:
              one_hot_actions = tf.one_hot(acs_ph, self.n_actions)
              actions_ph = tf.cast(one_hot_actions, tf.float32)
          else:
              actions_ph = acs_ph
  
          _input = tf.concat(
              [obs, actions_ph], axis=1
          )  # concatenate the two input -> form a transition
          p_h1 = tf.contrib.layers.fully_connected(
              _input, self.hidden_size, activation_fn=tf.nn.tanh
          )
          p_h2 = tf.contrib.layers.fully_connected(
              p_h1, self.hidden_size, activation_fn=tf.nn.tanh
          )
          logits = tf.contrib.layers.fully_connected(
              p_h2, 1, activation_fn=tf.identity
          )
      return logits
  
file_path: stable-baselines/stable_baselines/gail/adversary.py
imports: import gym
  import tensorflow
  import numpy
  from stable_baselines.common.mpi_running_mean_std import RunningMeanStd
  from stable_baselines.common import tf_util
label_desc: Use image node_id 2 for calling the TransitionClassifier obj's underlying member method code with example usage: obj.build_graph(obs_ph, acs_ph, reuse) and returns: logits