code: def get_actions(
      self,
      o,
      ag,
      g,
      noise_eps=0.0,
      random_eps=0.0,
      use_target_net=False,
      compute_Q=False,
  ):
      o, g = self._preprocess_og(o, ag, g)
      policy = self.target if use_target_net else self.main
      # values to compute
      vals = [policy.pi_tf]
      if compute_Q:
          vals += [policy.Q_pi_tf]
      # feed
      feed = {
          policy.o_tf: o.reshape(-1, self.dimo),
          policy.g_tf: g.reshape(-1, self.dimg),
          policy.u_tf: np.zeros(
              (o.size // self.dimo, self.dimu), dtype=np.float32
          ),
      }
  
      ret = self.sess.run(vals, feed_dict=feed)
      # action postprocessing
      u = ret[0]
      noise = (
          noise_eps * self.max_u * np.random.randn(*u.shape)
      )  # gaussian noise
      u += noise
      u = np.clip(u, -self.max_u, self.max_u)
      u += np.random.binomial(1, random_eps, u.shape[0]).reshape(
          -1, 1
      ) * (
          self._random_action(u.shape[0]) - u
      )  # eps-greedy
      if u.shape[0] == 1:
          u = u[0]
      u = u.copy()
      ret[0] = u
  
      if len(ret) == 1:
          return ret[0]
      else:
          return ret
  
imports: from collections import OrderedDict
  import numpy
  import tensorflow
  from tensorflow.contrib.staging import StagingArea
  from baselines import logger
  from baselines.her.util import import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major
  from baselines.her.normalizer import Normalizer
  from baselines.her.replay_buffer import ReplayBuffer
  from baselines.common.mpi_adam import MpiAdam
  from baselines.common import tf_util
label_desc: Use image node_id 5 for calling the DDPG obj's underlying member method code with example usage: obj.get_actions(o, ag, g, noise_eps, random_eps, use_target_net, compute_Q) and returns: ret, ret