code: def act(self, obs):
      dy.renew_cg()
      action = self.actor(obs).npvalue()
      if self.noise_stddev > 0:
          noise = np.random.randn(self.action_dim) * self.noise_stddev
          action += noise
      return np.clip(action, -1, 1)
  
file_path: dynet/examples/reinforcement-learning/ddpg.py
imports: import dynet
  import numpy
  from memory import Memory
  from network import MLP
label_desc: Use image node_id 2 for calling the DDPG obj's underlying member method code with example usage: obj.act(obs) and returns: np