class_docstr: the network for evaluate values of state-action pairs: Q(s,a) 
class_docstr_tok: ["the","network","for","evaluate","values","of","state-action","pairs",":","Q","(","s",",","a",")"]
code: def forward(self, input):
      x = self.linear1(input)
      x = self.linear2(x)
      x = self.linear3(x)
      return x
  
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 2 for calling the SoftQNetwork obj's underlying member method code with example usage: obj.forward(input) and returns: x