code: def _grads(self):
      # Avoid feed_dict here for performance!
      critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run(
          [
              self.Q_loss_tf,
              self.main.Q_pi_tf,
              self.Q_grad_tf,
              self.pi_grad_tf,
          ]
      )
      return critic_loss, actor_loss, Q_grad, pi_grad
  
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 10 for calling the DDPG obj's underlying member method code with example usage: obj._grads() and returns: critic_loss,  actor_loss,  Q_grad,  pi_grad