code: def learn(self, state, reward, state_, done):
      d = 0 if done else 1
      v_ = self.model(np.array([state_]))
      with tf.GradientTape() as tape:
          v = self.model(np.array([state]))
          ## TD_error = r + d * lambda * V(newS) - V(S)
          td_error = reward + d * LAM * v_ - v
          loss = tf.square(td_error)
      grad = tape.gradient(loss, self.model.trainable_weights)
      self.optimizer.apply_gradients(
          zip(grad, self.model.trainable_weights)
      )
      return td_error
  
imports: import argparse
  import time
  import matplotlib.pyplot
  import os
  import gym
  import numpy
  import tensorflow
  import tensorlayer
label_desc: Use image node_id 2 for calling the Critic obj's underlying member method code with example usage: obj.learn(state, reward, state_, done) and returns: td_error