class_docstr: the network for generating non-deterministic (Gaussian distributed) action from the state input 
class_docstr_tok: ["the","network","for","generating","non-deterministic","(","Gaussian","distributed",")","action","from","the","state","input"]
code: def __init__(
      self,
      num_inputs,
      num_actions,
      hidden_dim,
      action_range=1.0,
      init_w=3e-3,
      log_std_min=-20,
      log_std_max=2,
  ):
      super(PolicyNetwork, self).__init__()
  
      self.log_std_min = log_std_min
      self.log_std_max = log_std_max
  
      w_init = tf.keras.initializers.glorot_normal(seed=None)
      # w_init = tf.random_uniform_initializer(-init_w, init_w)
  
      self.linear1 = Dense(
          n_units=hidden_dim,
          act=tf.nn.relu,
          W_init=w_init,
          in_channels=num_inputs,
          name="policy1",
      )
      self.linear2 = Dense(
          n_units=hidden_dim,
          act=tf.nn.relu,
          W_init=w_init,
          in_channels=hidden_dim,
          name="policy2",
      )
      self.linear3 = Dense(
          n_units=hidden_dim,
          act=tf.nn.relu,
          W_init=w_init,
          in_channels=hidden_dim,
          name="policy3",
      )
  
      self.mean_linear = Dense(
          n_units=num_actions,
          W_init=w_init,
          b_init=tf.random_uniform_initializer(-init_w, init_w),
          in_channels=hidden_dim,
          name="policy_mean",
      )
      self.log_std_linear = Dense(
          n_units=num_actions,
          W_init=w_init,
          b_init=tf.random_uniform_initializer(-init_w, init_w),
          in_channels=hidden_dim,
          name="policy_logstd",
      )
  
      self.action_range = action_range
      self.num_actions = num_actions
  
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 PolicyNetwork object from inherited base classes: Model with example: obj = PolicyNetwork(num_inputs, num_actions, hidden_dim, action_range, init_w, log_std_min, log_std_max)