code: def __init__(
      self,
      state_dim,
      action_dim,
      action_range,
      hidden_dim,
      replay_buffer,
      SOFT_Q_LR=3e-4,
      POLICY_LR=3e-4,
      ALPHA_LR=3e-4,
  ):
      self.replay_buffer = replay_buffer
  
      # initialize all networks
      self.soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim)
      self.soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim)
      self.target_soft_q_net1 = SoftQNetwork(
          state_dim, action_dim, hidden_dim
      )
      self.target_soft_q_net2 = SoftQNetwork(
          state_dim, action_dim, hidden_dim
      )
      self.policy_net = PolicyNetwork(
          state_dim, action_dim, hidden_dim, action_range
      )
      self.soft_q_net1.train()
      self.soft_q_net2.train()
      self.target_soft_q_net1.eval()
      self.target_soft_q_net2.eval()
      self.policy_net.train()
  
      self.log_alpha = tf.Variable(
          0, dtype=np.float32, name="log_alpha"
      )
      self.alpha = tf.math.exp(self.log_alpha)
      print("Soft Q Network (1,2): ", self.soft_q_net1)
      print("Policy Network: ", self.policy_net)
      # set mode
      self.soft_q_net1.train()
      self.soft_q_net2.train()
      self.target_soft_q_net1.eval()
      self.target_soft_q_net2.eval()
      self.policy_net.train()
  
      # initialize weights of target networks
      self.target_soft_q_net1 = self.target_ini(
          self.soft_q_net1, self.target_soft_q_net1
      )
      self.target_soft_q_net2 = self.target_ini(
          self.soft_q_net2, self.target_soft_q_net2
      )
  
      self.soft_q_optimizer1 = tf.optimizers.Adam(SOFT_Q_LR)
      self.soft_q_optimizer2 = tf.optimizers.Adam(SOFT_Q_LR)
      self.policy_optimizer = tf.optimizers.Adam(POLICY_LR)
      self.alpha_optimizer = tf.optimizers.Adam(ALPHA_LR)
  
imports: import argparse
  import os
  import random
  import time
  import gym
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorflow_probability
  import tensorlayer
  from tensorlayer.layers import Dense
  from tensorlayer.models import Model
label_desc: Use image node_id 1 to create a new SAC object  with example: obj = SAC(state_dim, action_dim, action_range, hidden_dim, replay_buffer, SOFT_Q_LR, POLICY_LR, ALPHA_LR)