docstr: Predict the reward using the observation and action
  
  :param obs: (tf.Tensor or np.ndarray) the observation
  :param actions: (tf.Tensor or np.ndarray) the action
  :return: (np.ndarray) the reward
docstr_tok: ["Predict","the","reward","using","the","observation","and","action",":","param","obs",":","(","tf.Tensor","or","np.ndarray",")","the","observation",":","param","actions",":","(","tf.Tensor","or","np.ndarray",")","the","action",":","return",":","(","np.ndarray",")","the","reward"]
code: def get_reward(self, obs, actions):
      """
      Predict the reward using the observation and action
  
      :param obs: (tf.Tensor or np.ndarray) the observation
      :param actions: (tf.Tensor or np.ndarray) the action
      :return: (np.ndarray) the reward
      """
      sess = tf.get_default_session()
      if len(obs.shape) == 1:
          obs = np.expand_dims(obs, 0)
      if len(actions.shape) == 1:
          actions = np.expand_dims(actions, 0)
      elif len(actions.shape) == 0:
          # one discrete action
          actions = np.expand_dims(actions, 0)
  
      feed_dict = {
          self.generator_obs_ph: obs,
          self.generator_acs_ph: actions,
      }
      reward = sess.run(self.reward_op, feed_dict)
      return reward
  
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 4 for calling the TransitionClassifier obj's underlying member method code with example usage: obj.get_reward(obs, actions) and returns: reward