code: def __init__(
      self,
      state_dim,
      action_dim,
      action_range,
      hidden_dim,
      replay_buffer,
      policy_target_update_interval=1,
      q_lr=3e-4,
      policy_lr=3e-4,
  ):
      self.replay_buffer = replay_buffer
  
      # initialize all networks
      self.q_net1 = QNetwork(state_dim, action_dim, hidden_dim)
      self.q_net2 = QNetwork(state_dim, action_dim, hidden_dim)
      self.target_q_net1 = QNetwork(state_dim, action_dim, hidden_dim)
      self.target_q_net2 = QNetwork(state_dim, action_dim, hidden_dim)
      self.policy_net = PolicyNetwork(
          state_dim, action_dim, hidden_dim, action_range
      )
      self.target_policy_net = PolicyNetwork(
          state_dim, action_dim, hidden_dim, action_range
      )
      print("Q Network (1,2): ", self.q_net1)
      print("Policy Network: ", self.policy_net)
  
      # initialize weights of target networks
      self.target_q_net1 = self.target_ini(
          self.q_net1, self.target_q_net1
      )
      self.target_q_net2 = self.target_ini(
          self.q_net2, self.target_q_net2
      )
      self.target_policy_net = self.target_ini(
          self.policy_net, self.target_policy_net
      )
  
      # set train mode
      self.q_net1.train()
      self.q_net2.train()
      self.target_q_net1.eval()
      self.target_q_net2.eval()
      self.policy_net.train()
      self.target_policy_net.eval()
  
      self.update_cnt = 0
      self.policy_target_update_interval = policy_target_update_interval
  
      self.q_optimizer1 = tf.optimizers.Adam(q_lr)
      self.q_optimizer2 = tf.optimizers.Adam(q_lr)
      self.policy_optimizer = tf.optimizers.Adam(policy_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 TD3 object  with example: obj = TD3(state_dim, action_dim, action_range, hidden_dim, replay_buffer, policy_target_update_interval, q_lr, policy_lr)