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"]
docstr: generate action with state for calculating gradients 
docstr_tok: ["generate","action","with","state","for","calculating","gradients"]
code: def evaluate(self, state, epsilon=1e-6):
      """generate action with state for calculating gradients"""
      state = state.astype(np.float32)
      mean, log_std = self.forward(state)
      std = tf.math.exp(
          log_std
      )  # no clip in evaluation, clip affects gradients flow
  
      normal = Normal(0, 1)
      z = normal.sample(mean.shape)
      action_0 = tf.math.tanh(
          mean + std * z
      )  # TanhNormal distribution as actions; reparameterization trick
      action = self.action_range * action_0
      # according to original paper, with an extra last term for normalizing different action range
      log_prob = (
          Normal(mean, std).log_prob(mean + std * z)
          - tf.math.log(1.0 - action_0**2 + epsilon)
          - np.log(self.action_range)
      )
      # both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
      # the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
      # needs sum up across the dim of actions to get 1 dim probability; or else use Multivariate Normal.
      log_prob = tf.reduce_sum(log_prob, axis=1)[
          :, np.newaxis
      ]  # expand dim as reduce_sum causes 1 dim reduced
  
      return action, log_prob, z, mean, log_std
  
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 3 for calling the PolicyNetwork obj's underlying member method code with example usage: obj.evaluate(state, epsilon) and returns: action,  log_prob,  z,  mean,  log_std